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Browse files- .gitattributes +35 -0
- LICENSE +21 -0
- README.md +127 -0
- added_tokens.json +130 -0
- config.json +57 -0
- configuration_solar.py +206 -0
- generation_config.json +11 -0
- model-00001-of-00009.safetensors +3 -0
- model-00002-of-00009.safetensors +3 -0
- model-00003-of-00009.safetensors +3 -0
- model-00004-of-00009.safetensors +3 -0
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- model-00008-of-00009.safetensors +3 -0
- model-00009-of-00009.safetensors +3 -0
- model.safetensors.index.json +586 -0
- modeling_solar.py +1745 -0
- solar-pro-banner.png +0 -0
- special_tokens_map.json +30 -0
- tokenizer.json +0 -0
- tokenizer.model +3 -0
- tokenizer_config.json +1067 -0
- vllm_solar.py +552 -0
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LICENSE
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MIT License
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Copyright (c) Upstage Corporation.
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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README.md
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---
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license: mit
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license_link: https://huggingface.co/upstage/solar-pro-preview-instruct/blob/main/LICENSE
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language:
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- en
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pipeline_tag: text-generation
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tags:
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- nlp
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---
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<p align="left">
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<a href="https://go.upstage.ai/3Xk9J6X">
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<img src="https://huggingface.co/upstage/solar-pro-preview-instruct/resolve/main/solar-pro-banner.png" width="100%"/>
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</a>
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<p>
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# **Solar Pro Preview: The most intelligent LLM on a single GPU**
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# **Summary**
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We introduce **Solar Pro Preview**, an advanced large language model (LLM) with 22 billion parameters designed to [fit into a single GPU](https://www.upstage.ai/products/solar-pro-preview?utm_source=%08platform&utm_medium=huggingface&utm_campaign=solarpro-preview-launch). Solar Pro Preview shows superior performance compared to LLMs with less than 30 billion parameters and delivers performance comparable to models over three times its size, such as Llama 3.1 with 70 billion parameters.
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Solar Pro Preview is developed using an enhanced version of our previous depth up-scaling method, which scales a Phi-3-medium model with 14 billion parameters to 22 billion parameters, intended to run on a GPU with 80GB of VRAM. Our carefully curated training strategy and dataset have significantly enhanced performance from Phi-3-medium, particularly on the MMLU-Pro and IFEval benchmarks, both respected for evaluating a model’s knowledge and instruction-following abilities.
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Solar Pro Preview is a pre-release version of the official Solar Pro, with limitations on language coverage and a maximum context length of 4K. However, we believe Solar Pro Preview not only stands out as a highly efficient and capable model, but has the potential to be further extended to cover more languages and capabilities. The official version of Solar Pro will be released this November 2024 with expanded language support beyond English and longer context windows. To stay informed about the latest updates, please sign up for [our mailing list](https://www.upstage.ai/get-upstage-updates). If you have any feedback or questions about the model, please visit our [model discussion board](https://huggingface.co/upstage/solar-pro-preview-instruct/discussions) and connect with us directly.
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# **Usage**
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Solar Pro Preview is an instruction-tuned language model. This model is specifically designed to follow instructions and engage in conversational tasks.
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### Chat Template
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As an instruction-tuned model, Solar Pro Preview uses the ChatML template for optimal performance in conversational and instruction-following tasks. This approach aligns with the model's training data and is likely to yield more accurate and relevant responses. For instance, a question formatted in the ChatML template looks like the following, where the model generates the answer after <|im_start|>assistant. Solar Pro Preview does not thoroughly account for system prompts, which may result in the instruction in a system prompt being overlooked.
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```
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<|im_start|>user
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Please, introduce yourself.<|im_end|>
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<|im_start|>assistant
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```
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### Text Generation
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Below is an example inference code that details loading the model, applying the chat template, and generating the model answer.
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```python
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# Install requirements
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# !pip install transformers==4.44.2 torch==2.3.1 flash_attn==2.5.8 accelerate==0.31.0
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# Load model
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("upstage/solar-pro-preview-instruct")
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model = AutoModelForCausalLM.from_pretrained(
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"upstage/solar-pro-preview-instruct",
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device_map="cuda",
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torch_dtype="auto",
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trust_remote_code=True,
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)
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# Apply chat template
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messages = [
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{"role": "user", "content": "Please, introduce yourself."},
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]
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prompt = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device)
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# Generate text
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outputs = model.generate(prompt, max_new_tokens=512)
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print(tokenizer.decode(outputs[0]))
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```
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Solar Pro Preview is also available as an API in [Upstage Console](https://go.upstage.ai/3Xl0Hqv) and we provide other easy-to-use methods as well. If you'd like to explore these options, please visit our [blog page](https://www.upstage.ai/products/solar-pro-preview?utm_source=%08platform&utm_medium=huggingface&utm_campaign=solarpro-preview-launch).
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# **Evaluation**
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Solar Pro Preview is evaluated over a variety of benchmarks.
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| | Solar-pro-preview | Phi-3-medium-4K-instruct | Phi-3.5-MoE-instruct | Gemma 2 27B IT | Llama-3.1-8B-instruct | Llama-3.1-70B-instruct |
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| ------------- | :---------------: | :----------------------: | :------------------: | :----------------------------------------: | :-------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------: |
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| *Release Date* | 2024.09.08 | 2024.05.02 | 2024.08.20 | 2024.06.25 | 2024.06.18 | 2024.06.16 |
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| *Model size* | 22B | 14B | 41.9B (6.6B) | 27B | 8B | 70B |
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| *License* | MIT | MIT | MIT | [gemma](https://ai.google.dev/gemma/terms) | [llama3.1](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B/blob/main/LICENSE) | [llama3.1](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B/blob/main/LICENSE) |
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| **MMLU** | 79.14 | 78.02 | 78.66 | 76.13 | 68.25 | 82.09 |
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| **MMLU Pro** | 52.11 | 47.51 | 46.99 | 45.68 | 37.88 | 53.01 |
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| **IFEval** | 84.37 | 64.37 | 69.15 | 75.36 | 77.40 | 84.13 |
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| **ARC-C** | 68.86 | 66.55 | 68.34 | 74.06 | 60.24 | 70.39 |
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| **GPQA** | 36.38 | 35.78 | 34.38 | 36.38 | 35.26 | 41.06 |
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| **HellaSwag** | 86.36 | 85.68 | 85.97 | 86.02 | 80.08 | 86.42 |
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| **EQBench** | 77.91 | 76.78 | 77.22 | 80.32 | 65.80 | 82.52 |
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| **BigBench Hard** | 67.31 | 63.09 | 62.58 | 64.88 | 51.06 | 69.54 |
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| **MUSR** | 45.85 | 42.28 | 46.79 | 45.67 | 29.68 | 47.22 |
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| **GSM8K** | 89.69 | 84.76 | 82.26 | 62.85 | 75.97 | 92.12 |
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| **MBPP** | 61.59 | 60.27 | N/A (\*) | 63.08 | 52.20 | 65.51 |
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(*) Since the model tends to generate a chat template, the score can't be accurately determined.
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### Evaluation Protocol
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For easy reproduction of our evaluation results, we list the evaluation tools and settings used below. All evaluations are conducted with NVIDIA DGX H100.
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| | Evaluation setting | Metric | Evaluation tool |
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| ------------- | :-------------------- | :------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------- |
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| MMLU | 5-shot | macro_avg / acc | [lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/928e8bb6f50d1e93ef5d0bcaa81f8c5fd9a6f4d8) #928e8bb |
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| MMLU Pro | 5-shot | macro_avg / acc | [lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/928e8bb6f50d1e93ef5d0bcaa81f8c5fd9a6f4d8) #928e8bb |
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| IFEval | 0-shot, chat_template | mean of prompt_level_strict_acc and instruction_level_strict_acc | [lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/928e8bb6f50d1e93ef5d0bcaa81f8c5fd9a6f4d8) #928e8bb |
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| ARC-C | 25-shot | acc_norm | [lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/928e8bb6f50d1e93ef5d0bcaa81f8c5fd9a6f4d8) #928e8bb |
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| GPQA | 0-shot | acc_norm | [lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/928e8bb6f50d1e93ef5d0bcaa81f8c5fd9a6f4d8) #928e8bb |
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| HellaSwag | 10-shot | acc_norm | [lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/928e8bb6f50d1e93ef5d0bcaa81f8c5fd9a6f4d8) #928e8bb |
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| EQBench | 0-shot, chat_template | eqbench score | [lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/928e8bb6f50d1e93ef5d0bcaa81f8c5fd9a6f4d8) #928e8bb |
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| BigBench Hard | 3-shot | macro_avg / acc_norm | [lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/928e8bb6f50d1e93ef5d0bcaa81f8c5fd9a6f4d8) #928e8bb |
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| MUSR | 0-shot | macro_avg / acc_norm | [lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/928e8bb6f50d1e93ef5d0bcaa81f8c5fd9a6f4d8) #928e8bb |
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| GSM8K | 8-shot, CoT | acc, exact_match & strict_extract | [lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/928e8bb6f50d1e93ef5d0bcaa81f8c5fd9a6f4d8) #928e8bb |
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| MBPP | 0-shot | pass@1 | [bigcode-evaluation-harness](https://github.com/bigcode-project/bigcode-evaluation-harness/tree/0f3e95f0806e78a4f432056cdb1be93604a51d69) #0f3e95f |
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The results may vary slightly for different batch sizes and experimental environment such as GPU type.
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# **Contact us**
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For any questions and suggestions regarding the model, please visit the [discussion board](https://huggingface.co/upstage/solar-pro-preview-instruct/discussions).
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Learn more:
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- [Chat with Solar Pro](https://chat.upstage.ai)
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- [Solar Pro Preview developer documents](https://developers.upstage.ai/docs/apis/chat?utm_campaign=solarpro-preview-launch)
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Also try out:
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- [Document Parse](http://developers.upstage.ai/docs/apis/document-parse?utm_campaign=solarpro-preview-launch): An industry-leading model for converting complex document files to LLM-compatible HTML formats.
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- [Solar DocVision Preview](http://developers.upstage.ai/docs/apis/document-qa?utm_campaign=solarpro-preview-launch): A vision LLM specialized on documents.
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"<|assistant|>": 32001,
|
3 |
+
"<|end|>": 32000,
|
4 |
+
"<|im_end|>": 32007,
|
5 |
+
"<|im_start|>": 32010,
|
6 |
+
"<|placeholder100|>": 32104,
|
7 |
+
"<|placeholder101|>": 32105,
|
8 |
+
"<|placeholder102|>": 32106,
|
9 |
+
"<|placeholder103|>": 32107,
|
10 |
+
"<|placeholder104|>": 32108,
|
11 |
+
"<|placeholder105|>": 32109,
|
12 |
+
"<|placeholder106|>": 32110,
|
13 |
+
"<|placeholder107|>": 32111,
|
14 |
+
"<|placeholder108|>": 32112,
|
15 |
+
"<|placeholder109|>": 32113,
|
16 |
+
"<|placeholder10|>": 32014,
|
17 |
+
"<|placeholder110|>": 32114,
|
18 |
+
"<|placeholder111|>": 32115,
|
19 |
+
"<|placeholder112|>": 32116,
|
20 |
+
"<|placeholder113|>": 32117,
|
21 |
+
"<|placeholder114|>": 32118,
|
22 |
+
"<|placeholder115|>": 32119,
|
23 |
+
"<|placeholder116|>": 32120,
|
24 |
+
"<|placeholder117|>": 32121,
|
25 |
+
"<|placeholder118|>": 32122,
|
26 |
+
"<|placeholder119|>": 32123,
|
27 |
+
"<|placeholder11|>": 32015,
|
28 |
+
"<|placeholder120|>": 32124,
|
29 |
+
"<|placeholder121|>": 32125,
|
30 |
+
"<|placeholder122|>": 32126,
|
31 |
+
"<|placeholder123|>": 32127,
|
32 |
+
"<|placeholder12|>": 32016,
|
33 |
+
"<|placeholder13|>": 32017,
|
34 |
+
"<|placeholder14|>": 32018,
|
35 |
+
"<|placeholder15|>": 32019,
|
36 |
+
"<|placeholder16|>": 32020,
|
37 |
+
"<|placeholder17|>": 32021,
|
38 |
+
"<|placeholder18|>": 32022,
|
39 |
+
"<|placeholder19|>": 32023,
|
40 |
+
"<|placeholder1|>": 32002,
|
41 |
+
"<|placeholder20|>": 32024,
|
42 |
+
"<|placeholder21|>": 32025,
|
43 |
+
"<|placeholder22|>": 32026,
|
44 |
+
"<|placeholder23|>": 32027,
|
45 |
+
"<|placeholder24|>": 32028,
|
46 |
+
"<|placeholder25|>": 32029,
|
47 |
+
"<|placeholder26|>": 32030,
|
48 |
+
"<|placeholder27|>": 32031,
|
49 |
+
"<|placeholder28|>": 32032,
|
50 |
+
"<|placeholder29|>": 32033,
|
51 |
+
"<|placeholder2|>": 32003,
|
52 |
+
"<|placeholder30|>": 32034,
|
53 |
+
"<|placeholder31|>": 32035,
|
54 |
+
"<|placeholder32|>": 32036,
|
55 |
+
"<|placeholder33|>": 32037,
|
56 |
+
"<|placeholder34|>": 32038,
|
57 |
+
"<|placeholder35|>": 32039,
|
58 |
+
"<|placeholder36|>": 32040,
|
59 |
+
"<|placeholder37|>": 32041,
|
60 |
+
"<|placeholder38|>": 32042,
|
61 |
+
"<|placeholder39|>": 32043,
|
62 |
+
"<|placeholder3|>": 32004,
|
63 |
+
"<|placeholder40|>": 32044,
|
64 |
+
"<|placeholder41|>": 32045,
|
65 |
+
"<|placeholder42|>": 32046,
|
66 |
+
"<|placeholder43|>": 32047,
|
67 |
+
"<|placeholder44|>": 32048,
|
68 |
+
"<|placeholder45|>": 32049,
|
69 |
+
"<|placeholder46|>": 32050,
|
70 |
+
"<|placeholder47|>": 32051,
|
71 |
+
"<|placeholder48|>": 32052,
|
72 |
+
"<|placeholder49|>": 32053,
|
73 |
+
"<|placeholder4|>": 32005,
|
74 |
+
"<|placeholder50|>": 32054,
|
75 |
+
"<|placeholder51|>": 32055,
|
76 |
+
"<|placeholder52|>": 32056,
|
77 |
+
"<|placeholder53|>": 32057,
|
78 |
+
"<|placeholder54|>": 32058,
|
79 |
+
"<|placeholder55|>": 32059,
|
80 |
+
"<|placeholder56|>": 32060,
|
81 |
+
"<|placeholder57|>": 32061,
|
82 |
+
"<|placeholder58|>": 32062,
|
83 |
+
"<|placeholder59|>": 32063,
|
84 |
+
"<|placeholder5|>": 32008,
|
85 |
+
"<|placeholder60|>": 32064,
|
86 |
+
"<|placeholder61|>": 32065,
|
87 |
+
"<|placeholder62|>": 32066,
|
88 |
+
"<|placeholder63|>": 32067,
|
89 |
+
"<|placeholder64|>": 32068,
|
90 |
+
"<|placeholder65|>": 32069,
|
91 |
+
"<|placeholder66|>": 32070,
|
92 |
+
"<|placeholder67|>": 32071,
|
93 |
+
"<|placeholder68|>": 32072,
|
94 |
+
"<|placeholder69|>": 32073,
|
95 |
+
"<|placeholder6|>": 32009,
|
96 |
+
"<|placeholder70|>": 32074,
|
97 |
+
"<|placeholder71|>": 32075,
|
98 |
+
"<|placeholder72|>": 32076,
|
99 |
+
"<|placeholder73|>": 32077,
|
100 |
+
"<|placeholder74|>": 32078,
|
101 |
+
"<|placeholder75|>": 32079,
|
102 |
+
"<|placeholder76|>": 32080,
|
103 |
+
"<|placeholder77|>": 32081,
|
104 |
+
"<|placeholder78|>": 32082,
|
105 |
+
"<|placeholder79|>": 32083,
|
106 |
+
"<|placeholder7|>": 32011,
|
107 |
+
"<|placeholder80|>": 32084,
|
108 |
+
"<|placeholder81|>": 32085,
|
109 |
+
"<|placeholder82|>": 32086,
|
110 |
+
"<|placeholder83|>": 32087,
|
111 |
+
"<|placeholder84|>": 32088,
|
112 |
+
"<|placeholder85|>": 32089,
|
113 |
+
"<|placeholder86|>": 32090,
|
114 |
+
"<|placeholder87|>": 32091,
|
115 |
+
"<|placeholder88|>": 32092,
|
116 |
+
"<|placeholder89|>": 32093,
|
117 |
+
"<|placeholder8|>": 32012,
|
118 |
+
"<|placeholder90|>": 32094,
|
119 |
+
"<|placeholder91|>": 32095,
|
120 |
+
"<|placeholder92|>": 32096,
|
121 |
+
"<|placeholder93|>": 32097,
|
122 |
+
"<|placeholder94|>": 32098,
|
123 |
+
"<|placeholder95|>": 32099,
|
124 |
+
"<|placeholder96|>": 32100,
|
125 |
+
"<|placeholder97|>": 32101,
|
126 |
+
"<|placeholder98|>": 32102,
|
127 |
+
"<|placeholder99|>": 32103,
|
128 |
+
"<|placeholder9|>": 32013,
|
129 |
+
"<|system|>": 32006
|
130 |
+
}
|
config.json
ADDED
@@ -0,0 +1,57 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"architectures": [
|
3 |
+
"SolarForCausalLM"
|
4 |
+
],
|
5 |
+
"attention_bias": false,
|
6 |
+
"attention_dropout": 0.0,
|
7 |
+
"auto_map": {
|
8 |
+
"AutoConfig": "configuration_solar.SolarConfig",
|
9 |
+
"AutoModelForCausalLM": "modeling_solar.SolarForCausalLM"
|
10 |
+
},
|
11 |
+
"bos_token_id": 1,
|
12 |
+
"bskcn_1": [
|
13 |
+
12,
|
14 |
+
20,
|
15 |
+
32,
|
16 |
+
44
|
17 |
+
],
|
18 |
+
"bskcn_2": [
|
19 |
+
20,
|
20 |
+
32
|
21 |
+
],
|
22 |
+
"bskcn_3": [
|
23 |
+
16,
|
24 |
+
24,
|
25 |
+
36,
|
26 |
+
48
|
27 |
+
],
|
28 |
+
"bskcn_4": [
|
29 |
+
28,
|
30 |
+
40
|
31 |
+
],
|
32 |
+
"bskcn_tv": [
|
33 |
+
0.9,
|
34 |
+
0.8
|
35 |
+
],
|
36 |
+
"eos_token_id": 32007,
|
37 |
+
"hidden_act": "silu",
|
38 |
+
"hidden_size": 5120,
|
39 |
+
"initializer_range": 0.02,
|
40 |
+
"intermediate_size": 17920,
|
41 |
+
"max_position_embeddings": 4096,
|
42 |
+
"mlp_bias": false,
|
43 |
+
"model_type": "solar",
|
44 |
+
"num_attention_heads": 40,
|
45 |
+
"num_hidden_layers": 64,
|
46 |
+
"num_key_value_heads": 10,
|
47 |
+
"pretraining_tp": 1,
|
48 |
+
"rms_norm_eps": 1e-05,
|
49 |
+
"rope_scaling": null,
|
50 |
+
"rope_theta": 10000.0,
|
51 |
+
"sliding_window": 2047,
|
52 |
+
"tie_word_embeddings": false,
|
53 |
+
"torch_dtype": "bfloat16",
|
54 |
+
"transformers_version": "4.44.2",
|
55 |
+
"use_cache": true,
|
56 |
+
"vocab_size": 32128
|
57 |
+
}
|
configuration_solar.py
ADDED
@@ -0,0 +1,206 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# coding=utf-8
|
2 |
+
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
|
3 |
+
#
|
4 |
+
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
5 |
+
# and OPT implementations in this library. It has been modified from its
|
6 |
+
# original forms to accommodate minor architectural differences compared
|
7 |
+
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
8 |
+
#
|
9 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
10 |
+
# you may not use this file except in compliance with the License.
|
11 |
+
# You may obtain a copy of the License at
|
12 |
+
#
|
13 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
14 |
+
#
|
15 |
+
# Unless required by applicable law or agreed to in writing, software
|
16 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
17 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
18 |
+
# See the License for the specific language governing permissions and
|
19 |
+
# limitations under the License.
|
20 |
+
"""Solar model configuration"""
|
21 |
+
|
22 |
+
from transformers.configuration_utils import PretrainedConfig
|
23 |
+
from transformers.utils import logging
|
24 |
+
|
25 |
+
|
26 |
+
logger = logging.get_logger(__name__)
|
27 |
+
|
28 |
+
|
29 |
+
class SolarConfig(PretrainedConfig):
|
30 |
+
r"""
|
31 |
+
This is the configuration class to store the configuration of a [`SolarModel`]. It is used to instantiate an LLaMA
|
32 |
+
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
|
33 |
+
defaults will yield a similar configuration to that of the LLaMA-7B.
|
34 |
+
|
35 |
+
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
36 |
+
documentation from [`PretrainedConfig`] for more information.
|
37 |
+
|
38 |
+
|
39 |
+
Args:
|
40 |
+
vocab_size (`int`, *optional*, defaults to 32000):
|
41 |
+
Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the
|
42 |
+
`inputs_ids` passed when calling [`SolarModel`]
|
43 |
+
hidden_size (`int`, *optional*, defaults to 4096):
|
44 |
+
Dimension of the hidden representations.
|
45 |
+
intermediate_size (`int`, *optional*, defaults to 11008):
|
46 |
+
Dimension of the MLP representations.
|
47 |
+
num_hidden_layers (`int`, *optional*, defaults to 32):
|
48 |
+
Number of hidden layers in the Transformer decoder.
|
49 |
+
num_attention_heads (`int`, *optional*, defaults to 32):
|
50 |
+
Number of attention heads for each attention layer in the Transformer decoder.
|
51 |
+
num_key_value_heads (`int`, *optional*):
|
52 |
+
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
|
53 |
+
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
|
54 |
+
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
|
55 |
+
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
|
56 |
+
by meanpooling all the original heads within that group. For more details checkout [this
|
57 |
+
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
|
58 |
+
`num_attention_heads`.
|
59 |
+
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
|
60 |
+
The non-linear activation function (function or string) in the decoder.
|
61 |
+
max_position_embeddings (`int`, *optional*, defaults to 2048):
|
62 |
+
The maximum sequence length that this model might ever be used with. Solar 1 supports up to 2048 tokens,
|
63 |
+
Solar 2 up to 4096, CodeSolar up to 16384.
|
64 |
+
initializer_range (`float`, *optional*, defaults to 0.02):
|
65 |
+
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
66 |
+
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
|
67 |
+
The epsilon used by the rms normalization layers.
|
68 |
+
use_cache (`bool`, *optional*, defaults to `True`):
|
69 |
+
Whether or not the model should return the last key/values attentions (not used by all models). Only
|
70 |
+
relevant if `config.is_decoder=True`.
|
71 |
+
pad_token_id (`int`, *optional*):
|
72 |
+
Padding token id.
|
73 |
+
bos_token_id (`int`, *optional*, defaults to 1):
|
74 |
+
Beginning of stream token id.
|
75 |
+
eos_token_id (`int`, *optional*, defaults to 2):
|
76 |
+
End of stream token id.
|
77 |
+
pretraining_tp (`int`, *optional*, defaults to 1):
|
78 |
+
Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
|
79 |
+
document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to understand more about it. This value is
|
80 |
+
necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
|
81 |
+
issue](https://github.com/pytorch/pytorch/issues/76232).
|
82 |
+
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
|
83 |
+
Whether to tie weight embeddings
|
84 |
+
rope_theta (`float`, *optional*, defaults to 10000.0):
|
85 |
+
The base period of the RoPE embeddings.
|
86 |
+
rope_scaling (`Dict`, *optional*):
|
87 |
+
Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
|
88 |
+
strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
|
89 |
+
`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
|
90 |
+
`max_position_embeddings` to the expected new maximum. See the following thread for more information on how
|
91 |
+
these scaling strategies behave:
|
92 |
+
https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
|
93 |
+
experimental feature, subject to breaking API changes in future versions.
|
94 |
+
attention_bias (`bool`, *optional*, defaults to `False`):
|
95 |
+
Whether to use a bias in the query, key, value and output projection layers during self-attention.
|
96 |
+
attention_dropout (`float`, *optional*, defaults to 0.0):
|
97 |
+
The dropout ratio for the attention probabilities.
|
98 |
+
mlp_bias (`bool`, *optional*, defaults to `False`):
|
99 |
+
Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
|
100 |
+
sliding_window (`int`, *optional*, defaults to 2047):
|
101 |
+
Sliding window attention window size. If not specified, will default to `2047`.
|
102 |
+
|
103 |
+
```python
|
104 |
+
>>> from transformers import SolarModel, SolarConfig
|
105 |
+
|
106 |
+
>>> # Initializing a Solar-pro style configuration
|
107 |
+
>>> configuration = SolarConfig()
|
108 |
+
|
109 |
+
>>> # Initializing a model from the Solar-pro style configuration
|
110 |
+
>>> model = SolarModel(configuration)
|
111 |
+
|
112 |
+
>>> # Accessing the model configuration
|
113 |
+
>>> configuration = model.config
|
114 |
+
```"""
|
115 |
+
|
116 |
+
model_type = "solar"
|
117 |
+
keys_to_ignore_at_inference = ["past_key_values"]
|
118 |
+
|
119 |
+
def __init__(
|
120 |
+
self,
|
121 |
+
vocab_size=32000,
|
122 |
+
hidden_size=4096,
|
123 |
+
intermediate_size=11008,
|
124 |
+
num_hidden_layers=32,
|
125 |
+
num_attention_heads=32,
|
126 |
+
num_key_value_heads=None,
|
127 |
+
hidden_act="silu",
|
128 |
+
max_position_embeddings=2048,
|
129 |
+
initializer_range=0.02,
|
130 |
+
rms_norm_eps=1e-6,
|
131 |
+
use_cache=True,
|
132 |
+
pad_token_id=None,
|
133 |
+
bos_token_id=1,
|
134 |
+
eos_token_id=2,
|
135 |
+
pretraining_tp=1,
|
136 |
+
tie_word_embeddings=False,
|
137 |
+
rope_theta=10000.0,
|
138 |
+
rope_scaling=None,
|
139 |
+
attention_bias=False,
|
140 |
+
attention_dropout=0.0,
|
141 |
+
mlp_bias=False,
|
142 |
+
sliding_window=2047,
|
143 |
+
bskcn_1=[12, 20, 32, 44],
|
144 |
+
bskcn_2=[20, 32],
|
145 |
+
bskcn_3=[16, 24, 36, 48],
|
146 |
+
bskcn_4=[28, 40],
|
147 |
+
bskcn_tv=[0.9,0.8],
|
148 |
+
**kwargs,
|
149 |
+
):
|
150 |
+
self.vocab_size = vocab_size
|
151 |
+
self.max_position_embeddings = max_position_embeddings
|
152 |
+
self.hidden_size = hidden_size
|
153 |
+
self.intermediate_size = intermediate_size
|
154 |
+
self.num_hidden_layers = num_hidden_layers
|
155 |
+
self.num_attention_heads = num_attention_heads
|
156 |
+
|
157 |
+
# for backward compatibility
|
158 |
+
if num_key_value_heads is None:
|
159 |
+
num_key_value_heads = num_attention_heads
|
160 |
+
|
161 |
+
self.num_key_value_heads = num_key_value_heads
|
162 |
+
self.hidden_act = hidden_act
|
163 |
+
self.initializer_range = initializer_range
|
164 |
+
self.rms_norm_eps = rms_norm_eps
|
165 |
+
self.pretraining_tp = pretraining_tp
|
166 |
+
self.use_cache = use_cache
|
167 |
+
self.rope_theta = rope_theta
|
168 |
+
self.rope_scaling = rope_scaling
|
169 |
+
self._rope_scaling_validation()
|
170 |
+
self.attention_bias = attention_bias
|
171 |
+
self.attention_dropout = attention_dropout
|
172 |
+
self.mlp_bias = mlp_bias
|
173 |
+
self.sliding_window = sliding_window
|
174 |
+
self.bskcn_1 = bskcn_1
|
175 |
+
self.bskcn_2 = bskcn_2
|
176 |
+
self.bskcn_3 = bskcn_3
|
177 |
+
self.bskcn_4 = bskcn_4
|
178 |
+
self.bskcn_tv = bskcn_tv
|
179 |
+
|
180 |
+
super().__init__(
|
181 |
+
pad_token_id=pad_token_id,
|
182 |
+
bos_token_id=bos_token_id,
|
183 |
+
eos_token_id=eos_token_id,
|
184 |
+
tie_word_embeddings=tie_word_embeddings,
|
185 |
+
**kwargs,
|
186 |
+
)
|
187 |
+
|
188 |
+
def _rope_scaling_validation(self):
|
189 |
+
"""
|
190 |
+
Validate the `rope_scaling` configuration.
|
191 |
+
"""
|
192 |
+
if self.rope_scaling is None:
|
193 |
+
return
|
194 |
+
|
195 |
+
if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
|
196 |
+
raise ValueError(
|
197 |
+
"`rope_scaling` must be a dictionary with two fields, `type` and `factor`, " f"got {self.rope_scaling}"
|
198 |
+
)
|
199 |
+
rope_scaling_type = self.rope_scaling.get("type", None)
|
200 |
+
rope_scaling_factor = self.rope_scaling.get("factor", None)
|
201 |
+
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
|
202 |
+
raise ValueError(
|
203 |
+
f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
|
204 |
+
)
|
205 |
+
if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
|
206 |
+
raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")
|
generation_config.json
ADDED
@@ -0,0 +1,11 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"_from_model_config": true,
|
3 |
+
"bos_token_id": 1,
|
4 |
+
"eos_token_id": [
|
5 |
+
2,
|
6 |
+
32000,
|
7 |
+
32007
|
8 |
+
],
|
9 |
+
"use_cache": true,
|
10 |
+
"transformers_version": "4.44.2"
|
11 |
+
}
|
model-00001-of-00009.safetensors
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
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|
3 |
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size 4916640672
|
model-00002-of-00009.safetensors
ADDED
@@ -0,0 +1,3 @@
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+
version https://git-lfs.github.com/spec/v1
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|
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size 4954693120
|
model-00003-of-00009.safetensors
ADDED
@@ -0,0 +1,3 @@
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|
1 |
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version https://git-lfs.github.com/spec/v1
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size 4902243992
|
model-00004-of-00009.safetensors
ADDED
@@ -0,0 +1,3 @@
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|
1 |
+
version https://git-lfs.github.com/spec/v1
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|
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size 4954672440
|
model-00005-of-00009.safetensors
ADDED
@@ -0,0 +1,3 @@
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1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:9a93c34d1340d596ca741a2bf037ca3892b30e6166b745dd2f41d99de5309e08
|
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size 4954672432
|
model-00006-of-00009.safetensors
ADDED
@@ -0,0 +1,3 @@
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|
1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:813a5b5c0623d9fe090972c27e535147be0e76ebaaa3f90847dfaa02bded02b1
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size 4954693144
|
model-00007-of-00009.safetensors
ADDED
@@ -0,0 +1,3 @@
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|
|
|
|
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|
1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:b4c5b2f4d811d8e1568b745382b532dc911ce905af109bdabd6357de55466b61
|
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size 4902243992
|
model-00008-of-00009.safetensors
ADDED
@@ -0,0 +1,3 @@
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|
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|
|
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|
1 |
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version https://git-lfs.github.com/spec/v1
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|
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size 4954672440
|
model-00009-of-00009.safetensors
ADDED
@@ -0,0 +1,3 @@
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|
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version https://git-lfs.github.com/spec/v1
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size 4785599296
|
model.safetensors.index.json
ADDED
@@ -0,0 +1,586 @@
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|
|
|
|
|
|
|
|
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|
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|
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|
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|
|
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|
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|
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|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
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|
|
|
|
|
|
|
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|
|
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|
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|
|
|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
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|
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|
|
|
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|
|
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|
|
|
|
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|
|
|
|
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|
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|
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|
|
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|
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|
|
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|
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|
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|
|
|
|
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|
|
|
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|
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|
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|
|
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|
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|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
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|
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|
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|
|
|
|
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|
|
|
|
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|
|
|
|
|
|
1 |
+
{
|
2 |
+
"metadata": {
|
3 |
+
"total_size": 44280064000
|
4 |
+
},
|
5 |
+
"weight_map": {
|
6 |
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"lm_head.weight": "model-00009-of-00009.safetensors",
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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585 |
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}
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586 |
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}
|
modeling_solar.py
ADDED
@@ -0,0 +1,1745 @@
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|
1 |
+
# coding=utf-8
|
2 |
+
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
|
3 |
+
#
|
4 |
+
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
5 |
+
# and OPT implementations in this library. It has been modified from its
|
6 |
+
# original forms to accommodate minor architectural differences compared
|
7 |
+
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
8 |
+
#
|
9 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
10 |
+
# you may not use this file except in compliance with the License.
|
11 |
+
# You may obtain a copy of the License at
|
12 |
+
#
|
13 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
14 |
+
#
|
15 |
+
# Unless required by applicable law or agreed to in writing, software
|
16 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
17 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
18 |
+
# See the License for the specific language governing permissions and
|
19 |
+
# limitations under the License.
|
20 |
+
"""PyTorch Solar model."""
|
21 |
+
import math
|
22 |
+
from typing import List, Optional, Tuple, Union
|
23 |
+
|
24 |
+
import torch
|
25 |
+
import torch.nn.functional as F
|
26 |
+
import torch.utils.checkpoint
|
27 |
+
from torch import nn
|
28 |
+
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
|
29 |
+
|
30 |
+
from transformers.activations import ACT2FN
|
31 |
+
from transformers.cache_utils import Cache, DynamicCache, SlidingWindowCache, StaticCache
|
32 |
+
from transformers.modeling_attn_mask_utils import AttentionMaskConverter
|
33 |
+
from transformers.modeling_outputs import (
|
34 |
+
BaseModelOutputWithPast,
|
35 |
+
CausalLMOutputWithPast,
|
36 |
+
QuestionAnsweringModelOutput,
|
37 |
+
SequenceClassifierOutputWithPast,
|
38 |
+
TokenClassifierOutput,
|
39 |
+
)
|
40 |
+
from transformers.modeling_utils import PreTrainedModel
|
41 |
+
from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
|
42 |
+
from transformers.utils import (
|
43 |
+
add_start_docstrings,
|
44 |
+
add_start_docstrings_to_model_forward,
|
45 |
+
is_flash_attn_2_available,
|
46 |
+
is_flash_attn_greater_or_equal_2_10,
|
47 |
+
logging,
|
48 |
+
replace_return_docstrings,
|
49 |
+
)
|
50 |
+
from .configuration_solar import SolarConfig
|
51 |
+
|
52 |
+
|
53 |
+
if is_flash_attn_2_available():
|
54 |
+
from flash_attn import flash_attn_func, flash_attn_varlen_func
|
55 |
+
from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
|
56 |
+
import inspect
|
57 |
+
|
58 |
+
_flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters)
|
59 |
+
|
60 |
+
logger = logging.get_logger(__name__)
|
61 |
+
|
62 |
+
_CONFIG_FOR_DOC = "SolarConfig"
|
63 |
+
|
64 |
+
# Copied from transformers.models.llama.modeling_llama._get_unpad_data
|
65 |
+
def _get_unpad_data(attention_mask):
|
66 |
+
seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
|
67 |
+
indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
|
68 |
+
max_seqlen_in_batch = seqlens_in_batch.max().item()
|
69 |
+
cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
|
70 |
+
return (
|
71 |
+
indices,
|
72 |
+
cu_seqlens,
|
73 |
+
max_seqlen_in_batch,
|
74 |
+
)
|
75 |
+
|
76 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm
|
77 |
+
class SolarRMSNorm(nn.Module):
|
78 |
+
def __init__(self, hidden_size, eps=1e-6):
|
79 |
+
"""
|
80 |
+
SolarRMSNorm is equivalent to T5LayerNorm
|
81 |
+
"""
|
82 |
+
super().__init__()
|
83 |
+
self.weight = nn.Parameter(torch.ones(hidden_size))
|
84 |
+
self.variance_epsilon = eps
|
85 |
+
|
86 |
+
def forward(self, hidden_states):
|
87 |
+
input_dtype = hidden_states.dtype
|
88 |
+
hidden_states = hidden_states.to(torch.float32)
|
89 |
+
variance = hidden_states.pow(2).mean(-1, keepdim=True)
|
90 |
+
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
|
91 |
+
return self.weight * hidden_states.to(input_dtype)
|
92 |
+
|
93 |
+
|
94 |
+
ALL_LAYERNORM_LAYERS.append(SolarRMSNorm)
|
95 |
+
|
96 |
+
|
97 |
+
class SolarRotaryEmbedding(nn.Module):
|
98 |
+
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
|
99 |
+
super().__init__()
|
100 |
+
self.scaling_factor = scaling_factor
|
101 |
+
self.dim = dim
|
102 |
+
self.max_position_embeddings = max_position_embeddings
|
103 |
+
self.base = base
|
104 |
+
inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
|
105 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
106 |
+
# For BC we register cos and sin cached
|
107 |
+
self.max_seq_len_cached = max_position_embeddings
|
108 |
+
|
109 |
+
@torch.no_grad()
|
110 |
+
def forward(self, x, position_ids):
|
111 |
+
# x: [bs, num_attention_heads, seq_len, head_size]
|
112 |
+
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
|
113 |
+
position_ids_expanded = position_ids[:, None, :].float()
|
114 |
+
# Force float32 since bfloat16 loses precision on long contexts
|
115 |
+
# See https://github.com/huggingface/transformers/pull/29285
|
116 |
+
device_type = x.device.type
|
117 |
+
device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
|
118 |
+
with torch.autocast(device_type=device_type, enabled=False):
|
119 |
+
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
|
120 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
121 |
+
cos = emb.cos()
|
122 |
+
sin = emb.sin()
|
123 |
+
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
|
124 |
+
|
125 |
+
|
126 |
+
class SolarLinearScalingRotaryEmbedding(SolarRotaryEmbedding):
|
127 |
+
"""SolarRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
|
128 |
+
|
129 |
+
def forward(self, x, position_ids):
|
130 |
+
# difference to the original RoPE: a scaling factor is aplied to the position ids
|
131 |
+
position_ids = position_ids.float() / self.scaling_factor
|
132 |
+
cos, sin = super().forward(x, position_ids)
|
133 |
+
return cos, sin
|
134 |
+
|
135 |
+
|
136 |
+
class SolarDynamicNTKScalingRotaryEmbedding(SolarRotaryEmbedding):
|
137 |
+
"""SolarRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
|
138 |
+
|
139 |
+
def forward(self, x, position_ids):
|
140 |
+
# difference to the original RoPE: inv_freq is recomputed when the sequence length > original length
|
141 |
+
seq_len = torch.max(position_ids) + 1
|
142 |
+
if seq_len > self.max_position_embeddings:
|
143 |
+
base = self.base * (
|
144 |
+
(self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
|
145 |
+
) ** (self.dim / (self.dim - 2))
|
146 |
+
inv_freq = 1.0 / (
|
147 |
+
base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(x.device) / self.dim)
|
148 |
+
)
|
149 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False) # TODO joao: this may break with compilation
|
150 |
+
|
151 |
+
cos, sin = super().forward(x, position_ids)
|
152 |
+
return cos, sin
|
153 |
+
|
154 |
+
|
155 |
+
def rotate_half(x):
|
156 |
+
"""Rotates half the hidden dims of the input."""
|
157 |
+
x1 = x[..., : x.shape[-1] // 2]
|
158 |
+
x2 = x[..., x.shape[-1] // 2 :]
|
159 |
+
return torch.cat((-x2, x1), dim=-1)
|
160 |
+
|
161 |
+
|
162 |
+
def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
|
163 |
+
"""Applies Rotary Position Embedding to the query and key tensors.
|
164 |
+
|
165 |
+
Args:
|
166 |
+
q (`torch.Tensor`): The query tensor.
|
167 |
+
k (`torch.Tensor`): The key tensor.
|
168 |
+
cos (`torch.Tensor`): The cosine part of the rotary embedding.
|
169 |
+
sin (`torch.Tensor`): The sine part of the rotary embedding.
|
170 |
+
position_ids (`torch.Tensor`, *optional*):
|
171 |
+
Deprecated and unused.
|
172 |
+
unsqueeze_dim (`int`, *optional*, defaults to 1):
|
173 |
+
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
|
174 |
+
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
|
175 |
+
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
|
176 |
+
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
|
177 |
+
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
|
178 |
+
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
|
179 |
+
Returns:
|
180 |
+
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
|
181 |
+
"""
|
182 |
+
cos = cos.unsqueeze(unsqueeze_dim)
|
183 |
+
sin = sin.unsqueeze(unsqueeze_dim)
|
184 |
+
q_embed = (q * cos) + (rotate_half(q) * sin)
|
185 |
+
k_embed = (k * cos) + (rotate_half(k) * sin)
|
186 |
+
return q_embed, k_embed
|
187 |
+
|
188 |
+
|
189 |
+
class SolarMLP(nn.Module):
|
190 |
+
def __init__(self, config):
|
191 |
+
super().__init__()
|
192 |
+
self.config = config
|
193 |
+
self.hidden_size = config.hidden_size
|
194 |
+
self.intermediate_size = config.intermediate_size
|
195 |
+
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
|
196 |
+
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
|
197 |
+
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias)
|
198 |
+
self.act_fn = ACT2FN[config.hidden_act]
|
199 |
+
|
200 |
+
def forward(self, x):
|
201 |
+
if self.config.pretraining_tp > 1:
|
202 |
+
slice = self.intermediate_size // self.config.pretraining_tp
|
203 |
+
gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
|
204 |
+
up_proj_slices = self.up_proj.weight.split(slice, dim=0)
|
205 |
+
down_proj_slices = self.down_proj.weight.split(slice, dim=1)
|
206 |
+
|
207 |
+
gate_proj = torch.cat(
|
208 |
+
[F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1
|
209 |
+
)
|
210 |
+
up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
|
211 |
+
|
212 |
+
intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
|
213 |
+
down_proj = [
|
214 |
+
F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
|
215 |
+
]
|
216 |
+
down_proj = sum(down_proj)
|
217 |
+
else:
|
218 |
+
down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
|
219 |
+
|
220 |
+
return down_proj
|
221 |
+
|
222 |
+
|
223 |
+
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
|
224 |
+
"""
|
225 |
+
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
|
226 |
+
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
|
227 |
+
"""
|
228 |
+
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
|
229 |
+
if n_rep == 1:
|
230 |
+
return hidden_states
|
231 |
+
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
|
232 |
+
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
|
233 |
+
|
234 |
+
|
235 |
+
class SolarAttention(nn.Module):
|
236 |
+
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
237 |
+
|
238 |
+
def __init__(self, config: SolarConfig, layer_idx: Optional[int] = None):
|
239 |
+
super().__init__()
|
240 |
+
self.config = config
|
241 |
+
self.layer_idx = layer_idx
|
242 |
+
if layer_idx is None:
|
243 |
+
logger.warning_once(
|
244 |
+
f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
|
245 |
+
"lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
|
246 |
+
"when creating this class."
|
247 |
+
)
|
248 |
+
|
249 |
+
self.attention_dropout = config.attention_dropout
|
250 |
+
self.hidden_size = config.hidden_size
|
251 |
+
self.num_heads = config.num_attention_heads
|
252 |
+
self.head_dim = self.hidden_size // self.num_heads
|
253 |
+
self.num_key_value_heads = config.num_key_value_heads
|
254 |
+
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
|
255 |
+
self.max_position_embeddings = config.max_position_embeddings
|
256 |
+
self.rope_theta = config.rope_theta
|
257 |
+
self.is_causal = True
|
258 |
+
|
259 |
+
if (self.head_dim * self.num_heads) != self.hidden_size:
|
260 |
+
raise ValueError(
|
261 |
+
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
|
262 |
+
f" and `num_heads`: {self.num_heads})."
|
263 |
+
)
|
264 |
+
|
265 |
+
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
|
266 |
+
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
|
267 |
+
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
|
268 |
+
self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=config.attention_bias)
|
269 |
+
self._init_rope()
|
270 |
+
|
271 |
+
def _init_rope(self):
|
272 |
+
if self.config.rope_scaling is None:
|
273 |
+
self.rotary_emb = SolarRotaryEmbedding(
|
274 |
+
self.head_dim,
|
275 |
+
max_position_embeddings=self.max_position_embeddings,
|
276 |
+
base=self.rope_theta,
|
277 |
+
)
|
278 |
+
else:
|
279 |
+
scaling_type = self.config.rope_scaling["type"]
|
280 |
+
scaling_factor = self.config.rope_scaling["factor"]
|
281 |
+
if scaling_type == "linear":
|
282 |
+
self.rotary_emb = SolarLinearScalingRotaryEmbedding(
|
283 |
+
self.head_dim,
|
284 |
+
max_position_embeddings=self.max_position_embeddings,
|
285 |
+
scaling_factor=scaling_factor,
|
286 |
+
base=self.rope_theta,
|
287 |
+
)
|
288 |
+
elif scaling_type == "dynamic":
|
289 |
+
self.rotary_emb = SolarDynamicNTKScalingRotaryEmbedding(
|
290 |
+
self.head_dim,
|
291 |
+
max_position_embeddings=self.max_position_embeddings,
|
292 |
+
scaling_factor=scaling_factor,
|
293 |
+
base=self.rope_theta,
|
294 |
+
)
|
295 |
+
else:
|
296 |
+
raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
|
297 |
+
|
298 |
+
def forward(
|
299 |
+
self,
|
300 |
+
hidden_states: torch.Tensor,
|
301 |
+
attention_mask: Optional[torch.Tensor] = None,
|
302 |
+
position_ids: Optional[torch.LongTensor] = None,
|
303 |
+
past_key_value: Optional[Cache] = None,
|
304 |
+
output_attentions: bool = False,
|
305 |
+
use_cache: bool = False,
|
306 |
+
cache_position: Optional[torch.LongTensor] = None,
|
307 |
+
**kwargs,
|
308 |
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
309 |
+
bsz, q_len, _ = hidden_states.size()
|
310 |
+
|
311 |
+
if self.config.pretraining_tp > 1:
|
312 |
+
key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
|
313 |
+
query_slices = self.q_proj.weight.split(
|
314 |
+
(self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
|
315 |
+
)
|
316 |
+
key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
|
317 |
+
value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
|
318 |
+
|
319 |
+
query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
|
320 |
+
query_states = torch.cat(query_states, dim=-1)
|
321 |
+
|
322 |
+
key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
|
323 |
+
key_states = torch.cat(key_states, dim=-1)
|
324 |
+
|
325 |
+
value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
|
326 |
+
value_states = torch.cat(value_states, dim=-1)
|
327 |
+
|
328 |
+
else:
|
329 |
+
query_states = self.q_proj(hidden_states)
|
330 |
+
key_states = self.k_proj(hidden_states)
|
331 |
+
value_states = self.v_proj(hidden_states)
|
332 |
+
|
333 |
+
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
334 |
+
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
335 |
+
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
336 |
+
|
337 |
+
cos, sin = self.rotary_emb(value_states, position_ids)
|
338 |
+
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
|
339 |
+
|
340 |
+
if past_key_value is not None:
|
341 |
+
# sin and cos are specific to RoPE models; cache_position needed for the static cache
|
342 |
+
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
|
343 |
+
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
344 |
+
|
345 |
+
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
346 |
+
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
347 |
+
|
348 |
+
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
349 |
+
|
350 |
+
if attention_mask is not None: # no matter the length, we just slice it
|
351 |
+
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
|
352 |
+
attn_weights = attn_weights + causal_mask
|
353 |
+
|
354 |
+
# upcast attention to fp32
|
355 |
+
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
|
356 |
+
attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
|
357 |
+
attn_output = torch.matmul(attn_weights, value_states)
|
358 |
+
|
359 |
+
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
|
360 |
+
raise ValueError(
|
361 |
+
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
|
362 |
+
f" {attn_output.size()}"
|
363 |
+
)
|
364 |
+
|
365 |
+
attn_output = attn_output.transpose(1, 2).contiguous()
|
366 |
+
|
367 |
+
attn_output = attn_output.reshape(bsz, q_len, -1)
|
368 |
+
|
369 |
+
if self.config.pretraining_tp > 1:
|
370 |
+
attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
|
371 |
+
o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
|
372 |
+
attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
|
373 |
+
else:
|
374 |
+
attn_output = self.o_proj(attn_output)
|
375 |
+
|
376 |
+
if not output_attentions:
|
377 |
+
attn_weights = None
|
378 |
+
|
379 |
+
return attn_output, attn_weights, past_key_value
|
380 |
+
|
381 |
+
# Copied from transformers.models.mistral.modeling_mistal.MistralFlashAttention2
|
382 |
+
class SolarFlashAttention2(SolarAttention):
|
383 |
+
"""
|
384 |
+
Solar flash attention module. This module inherits from `SolarAttention` as the weights of the module stays
|
385 |
+
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
|
386 |
+
flash attention and deal with padding tokens in case the input contains any of them.
|
387 |
+
"""
|
388 |
+
|
389 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
|
390 |
+
def __init__(self, *args, **kwargs):
|
391 |
+
super().__init__(*args, **kwargs)
|
392 |
+
|
393 |
+
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
|
394 |
+
# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
|
395 |
+
# Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
|
396 |
+
self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
|
397 |
+
|
398 |
+
def forward(
|
399 |
+
self,
|
400 |
+
hidden_states: torch.Tensor,
|
401 |
+
attention_mask: Optional[torch.Tensor] = None,
|
402 |
+
position_ids: Optional[torch.LongTensor] = None,
|
403 |
+
past_key_value: Optional[Cache] = None,
|
404 |
+
output_attentions: bool = False,
|
405 |
+
use_cache: bool = False,
|
406 |
+
cache_position: Optional[torch.LongTensor] = None,
|
407 |
+
):
|
408 |
+
if isinstance(past_key_value, StaticCache):
|
409 |
+
raise ValueError(
|
410 |
+
"`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` "
|
411 |
+
"make sure to use `sdpa` in the mean time, and open an issue at https://github.com/huggingface/transformers"
|
412 |
+
)
|
413 |
+
|
414 |
+
output_attentions = False
|
415 |
+
|
416 |
+
bsz, q_len, _ = hidden_states.size()
|
417 |
+
|
418 |
+
query_states = self.q_proj(hidden_states)
|
419 |
+
key_states = self.k_proj(hidden_states)
|
420 |
+
value_states = self.v_proj(hidden_states)
|
421 |
+
|
422 |
+
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
423 |
+
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
424 |
+
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
425 |
+
|
426 |
+
kv_seq_len = key_states.shape[-2]
|
427 |
+
if past_key_value is not None:
|
428 |
+
kv_seq_len += cache_position[0]
|
429 |
+
|
430 |
+
cos, sin = self.rotary_emb(value_states, position_ids)
|
431 |
+
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
|
432 |
+
|
433 |
+
use_sliding_windows = (
|
434 |
+
_flash_supports_window_size
|
435 |
+
and getattr(self.config, "sliding_window", None) is not None
|
436 |
+
and kv_seq_len > self.config.sliding_window
|
437 |
+
)
|
438 |
+
|
439 |
+
if not _flash_supports_window_size:
|
440 |
+
logger.warning_once(
|
441 |
+
"The current flash attention version does not support sliding window attention, for a more memory efficient implementation"
|
442 |
+
" make sure to upgrade flash-attn library."
|
443 |
+
)
|
444 |
+
|
445 |
+
if past_key_value is not None:
|
446 |
+
# Activate slicing cache only if the config has a value `sliding_windows` attribute
|
447 |
+
cache_has_contents = past_key_value.get_seq_length(self.layer_idx) > 0
|
448 |
+
if (
|
449 |
+
getattr(self.config, "sliding_window", None) is not None
|
450 |
+
and kv_seq_len > self.config.sliding_window
|
451 |
+
and cache_has_contents
|
452 |
+
):
|
453 |
+
slicing_tokens = 1 - self.config.sliding_window
|
454 |
+
|
455 |
+
past_key = past_key_value[self.layer_idx][0]
|
456 |
+
past_value = past_key_value[self.layer_idx][1]
|
457 |
+
|
458 |
+
past_key = past_key[:, :, slicing_tokens:, :].contiguous()
|
459 |
+
past_value = past_value[:, :, slicing_tokens:, :].contiguous()
|
460 |
+
|
461 |
+
if past_key.shape[-2] != self.config.sliding_window - 1:
|
462 |
+
raise ValueError(
|
463 |
+
f"past key must have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got"
|
464 |
+
f" {past_key.shape}"
|
465 |
+
)
|
466 |
+
|
467 |
+
if attention_mask is not None:
|
468 |
+
attention_mask = attention_mask[:, slicing_tokens:]
|
469 |
+
attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1)
|
470 |
+
|
471 |
+
cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
|
472 |
+
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
473 |
+
|
474 |
+
# repeat k/v heads if n_kv_heads < n_heads
|
475 |
+
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
476 |
+
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
477 |
+
dropout_rate = 0.0 if not self.training else self.attention_dropout
|
478 |
+
|
479 |
+
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
|
480 |
+
# therefore the input hidden states gets silently casted in float32. Hence, we need
|
481 |
+
# cast them back in float16 just to be sure everything works as expected.
|
482 |
+
input_dtype = query_states.dtype
|
483 |
+
if input_dtype == torch.float32:
|
484 |
+
if torch.is_autocast_enabled():
|
485 |
+
target_dtype = torch.get_autocast_gpu_dtype()
|
486 |
+
# Handle the case where the model is quantized
|
487 |
+
elif hasattr(self.config, "_pre_quantization_dtype"):
|
488 |
+
target_dtype = self.config._pre_quantization_dtype
|
489 |
+
else:
|
490 |
+
target_dtype = self.q_proj.weight.dtype
|
491 |
+
|
492 |
+
logger.warning_once(
|
493 |
+
f"The input hidden states seems to be silently casted in float32, this might be related to"
|
494 |
+
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
|
495 |
+
f" {target_dtype}."
|
496 |
+
)
|
497 |
+
|
498 |
+
query_states = query_states.to(target_dtype)
|
499 |
+
key_states = key_states.to(target_dtype)
|
500 |
+
value_states = value_states.to(target_dtype)
|
501 |
+
|
502 |
+
# Reashape to the expected shape for Flash Attention
|
503 |
+
query_states = query_states.transpose(1, 2)
|
504 |
+
key_states = key_states.transpose(1, 2)
|
505 |
+
value_states = value_states.transpose(1, 2)
|
506 |
+
|
507 |
+
attn_output = self._flash_attention_forward(
|
508 |
+
query_states,
|
509 |
+
key_states,
|
510 |
+
value_states,
|
511 |
+
attention_mask,
|
512 |
+
q_len,
|
513 |
+
dropout=dropout_rate,
|
514 |
+
use_sliding_windows=use_sliding_windows,
|
515 |
+
)
|
516 |
+
|
517 |
+
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
|
518 |
+
attn_output = self.o_proj(attn_output)
|
519 |
+
|
520 |
+
if not output_attentions:
|
521 |
+
attn_weights = None
|
522 |
+
|
523 |
+
return attn_output, attn_weights, past_key_value
|
524 |
+
|
525 |
+
def _flash_attention_forward(
|
526 |
+
self,
|
527 |
+
query_states,
|
528 |
+
key_states,
|
529 |
+
value_states,
|
530 |
+
attention_mask,
|
531 |
+
query_length,
|
532 |
+
dropout=0.0,
|
533 |
+
softmax_scale=None,
|
534 |
+
use_sliding_windows=False,
|
535 |
+
):
|
536 |
+
"""
|
537 |
+
Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
|
538 |
+
first unpad the input, then computes the attention scores and pad the final attention scores.
|
539 |
+
|
540 |
+
Args:
|
541 |
+
query_states (`torch.Tensor`):
|
542 |
+
Input query states to be passed to Flash Attention API
|
543 |
+
key_states (`torch.Tensor`):
|
544 |
+
Input key states to be passed to Flash Attention API
|
545 |
+
value_states (`torch.Tensor`):
|
546 |
+
Input value states to be passed to Flash Attention API
|
547 |
+
attention_mask (`torch.Tensor`):
|
548 |
+
The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
|
549 |
+
position of padding tokens and 1 for the position of non-padding tokens.
|
550 |
+
dropout (`float`):
|
551 |
+
Attention dropout
|
552 |
+
softmax_scale (`float`, *optional*):
|
553 |
+
The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
|
554 |
+
use_sliding_windows (`bool`, *optional*):
|
555 |
+
Whether to activate sliding window attention.
|
556 |
+
"""
|
557 |
+
if not self._flash_attn_uses_top_left_mask:
|
558 |
+
causal = self.is_causal
|
559 |
+
else:
|
560 |
+
# TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
|
561 |
+
causal = self.is_causal and query_length != 1
|
562 |
+
|
563 |
+
# Contains at least one padding token in the sequence
|
564 |
+
if attention_mask is not None:
|
565 |
+
batch_size = query_states.shape[0]
|
566 |
+
query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
|
567 |
+
query_states, key_states, value_states, attention_mask, query_length
|
568 |
+
)
|
569 |
+
|
570 |
+
cu_seqlens_q, cu_seqlens_k = cu_seq_lens
|
571 |
+
max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
|
572 |
+
|
573 |
+
if not use_sliding_windows:
|
574 |
+
attn_output_unpad = flash_attn_varlen_func(
|
575 |
+
query_states,
|
576 |
+
key_states,
|
577 |
+
value_states,
|
578 |
+
cu_seqlens_q=cu_seqlens_q,
|
579 |
+
cu_seqlens_k=cu_seqlens_k,
|
580 |
+
max_seqlen_q=max_seqlen_in_batch_q,
|
581 |
+
max_seqlen_k=max_seqlen_in_batch_k,
|
582 |
+
dropout_p=dropout,
|
583 |
+
softmax_scale=softmax_scale,
|
584 |
+
causal=causal,
|
585 |
+
)
|
586 |
+
else:
|
587 |
+
attn_output_unpad = flash_attn_varlen_func(
|
588 |
+
query_states,
|
589 |
+
key_states,
|
590 |
+
value_states,
|
591 |
+
cu_seqlens_q=cu_seqlens_q,
|
592 |
+
cu_seqlens_k=cu_seqlens_k,
|
593 |
+
max_seqlen_q=max_seqlen_in_batch_q,
|
594 |
+
max_seqlen_k=max_seqlen_in_batch_k,
|
595 |
+
dropout_p=dropout,
|
596 |
+
softmax_scale=softmax_scale,
|
597 |
+
causal=causal,
|
598 |
+
window_size=(self.config.sliding_window, self.config.sliding_window),
|
599 |
+
)
|
600 |
+
|
601 |
+
attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
|
602 |
+
else:
|
603 |
+
if not use_sliding_windows:
|
604 |
+
attn_output = flash_attn_func(
|
605 |
+
query_states,
|
606 |
+
key_states,
|
607 |
+
value_states,
|
608 |
+
dropout,
|
609 |
+
softmax_scale=softmax_scale,
|
610 |
+
causal=causal,
|
611 |
+
)
|
612 |
+
else:
|
613 |
+
attn_output = flash_attn_func(
|
614 |
+
query_states,
|
615 |
+
key_states,
|
616 |
+
value_states,
|
617 |
+
dropout,
|
618 |
+
softmax_scale=softmax_scale,
|
619 |
+
causal=causal,
|
620 |
+
window_size=(self.config.sliding_window, self.config.sliding_window),
|
621 |
+
)
|
622 |
+
|
623 |
+
return attn_output
|
624 |
+
|
625 |
+
def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
|
626 |
+
batch_size, kv_seq_len, num_heads, head_dim = key_layer.shape
|
627 |
+
|
628 |
+
# On the first iteration we need to properly re-create the padding mask
|
629 |
+
# by slicing it on the proper place
|
630 |
+
if kv_seq_len != attention_mask.shape[-1]:
|
631 |
+
attention_mask_num_tokens = attention_mask.shape[-1]
|
632 |
+
attention_mask = attention_mask[:, attention_mask_num_tokens - kv_seq_len :]
|
633 |
+
|
634 |
+
indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
|
635 |
+
|
636 |
+
key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
|
637 |
+
value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
|
638 |
+
|
639 |
+
if query_length == kv_seq_len:
|
640 |
+
query_layer = index_first_axis(
|
641 |
+
query_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k
|
642 |
+
)
|
643 |
+
cu_seqlens_q = cu_seqlens_k
|
644 |
+
max_seqlen_in_batch_q = max_seqlen_in_batch_k
|
645 |
+
indices_q = indices_k
|
646 |
+
elif query_length == 1:
|
647 |
+
max_seqlen_in_batch_q = 1
|
648 |
+
cu_seqlens_q = torch.arange(
|
649 |
+
batch_size + 1, dtype=torch.int32, device=query_layer.device
|
650 |
+
) # There is a memcpy here, that is very bad.
|
651 |
+
indices_q = cu_seqlens_q[:-1]
|
652 |
+
query_layer = query_layer.squeeze(1)
|
653 |
+
else:
|
654 |
+
# The -q_len: slice assumes left padding.
|
655 |
+
attention_mask = attention_mask[:, -query_length:]
|
656 |
+
query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
|
657 |
+
|
658 |
+
return (
|
659 |
+
query_layer,
|
660 |
+
key_layer,
|
661 |
+
value_layer,
|
662 |
+
indices_q,
|
663 |
+
(cu_seqlens_q, cu_seqlens_k),
|
664 |
+
(max_seqlen_in_batch_q, max_seqlen_in_batch_k),
|
665 |
+
)
|
666 |
+
|
667 |
+
|
668 |
+
class SolarSdpaAttention(SolarAttention):
|
669 |
+
"""
|
670 |
+
Solar attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
|
671 |
+
`SolarAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
|
672 |
+
SDPA API.
|
673 |
+
"""
|
674 |
+
|
675 |
+
# Adapted from SolarAttention.forward
|
676 |
+
def forward(
|
677 |
+
self,
|
678 |
+
hidden_states: torch.Tensor,
|
679 |
+
attention_mask: Optional[torch.Tensor] = None,
|
680 |
+
position_ids: Optional[torch.LongTensor] = None,
|
681 |
+
past_key_value: Optional[Cache] = None,
|
682 |
+
output_attentions: bool = False,
|
683 |
+
use_cache: bool = False,
|
684 |
+
cache_position: Optional[torch.LongTensor] = None,
|
685 |
+
**kwargs,
|
686 |
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
687 |
+
if output_attentions:
|
688 |
+
# TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
|
689 |
+
logger.warning_once(
|
690 |
+
"SolarModel is using SolarSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
|
691 |
+
'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
|
692 |
+
)
|
693 |
+
return super().forward(
|
694 |
+
hidden_states=hidden_states,
|
695 |
+
attention_mask=attention_mask,
|
696 |
+
position_ids=position_ids,
|
697 |
+
past_key_value=past_key_value,
|
698 |
+
output_attentions=output_attentions,
|
699 |
+
use_cache=use_cache,
|
700 |
+
cache_position=cache_position,
|
701 |
+
)
|
702 |
+
|
703 |
+
bsz, q_len, _ = hidden_states.size()
|
704 |
+
|
705 |
+
query_states = self.q_proj(hidden_states)
|
706 |
+
key_states = self.k_proj(hidden_states)
|
707 |
+
value_states = self.v_proj(hidden_states)
|
708 |
+
|
709 |
+
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
710 |
+
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
711 |
+
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
712 |
+
|
713 |
+
cos, sin = self.rotary_emb(value_states, position_ids)
|
714 |
+
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
|
715 |
+
|
716 |
+
if past_key_value is not None:
|
717 |
+
# sin and cos are specific to RoPE models; cache_position needed for the static cache
|
718 |
+
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
|
719 |
+
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
720 |
+
|
721 |
+
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
722 |
+
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
723 |
+
|
724 |
+
causal_mask = attention_mask
|
725 |
+
if attention_mask is not None:
|
726 |
+
causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
|
727 |
+
|
728 |
+
# SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
|
729 |
+
# Reference: https://github.com/pytorch/pytorch/issues/112577.
|
730 |
+
if query_states.device.type == "cuda" and causal_mask is not None:
|
731 |
+
query_states = query_states.contiguous()
|
732 |
+
key_states = key_states.contiguous()
|
733 |
+
value_states = value_states.contiguous()
|
734 |
+
|
735 |
+
# We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
|
736 |
+
# in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
|
737 |
+
is_causal = True if causal_mask is None and q_len > 1 else False
|
738 |
+
|
739 |
+
attn_output = torch.nn.functional.scaled_dot_product_attention(
|
740 |
+
query_states,
|
741 |
+
key_states,
|
742 |
+
value_states,
|
743 |
+
attn_mask=causal_mask,
|
744 |
+
dropout_p=self.attention_dropout if self.training else 0.0,
|
745 |
+
is_causal=is_causal,
|
746 |
+
)
|
747 |
+
|
748 |
+
attn_output = attn_output.transpose(1, 2).contiguous()
|
749 |
+
attn_output = attn_output.view(bsz, q_len, -1)
|
750 |
+
|
751 |
+
attn_output = self.o_proj(attn_output)
|
752 |
+
|
753 |
+
return attn_output, None, past_key_value
|
754 |
+
|
755 |
+
|
756 |
+
SOLAR_ATTENTION_CLASSES = {
|
757 |
+
"eager": SolarAttention,
|
758 |
+
"flash_attention_2": SolarFlashAttention2,
|
759 |
+
"sdpa": SolarSdpaAttention,
|
760 |
+
}
|
761 |
+
|
762 |
+
|
763 |
+
class SolarDecoderLayer(nn.Module):
|
764 |
+
def __init__(self, config: SolarConfig, layer_idx: int):
|
765 |
+
super().__init__()
|
766 |
+
self.hidden_size = config.hidden_size
|
767 |
+
|
768 |
+
self.self_attn = SOLAR_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
|
769 |
+
|
770 |
+
self.mlp = SolarMLP(config)
|
771 |
+
self.input_layernorm = SolarRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
772 |
+
self.post_attention_layernorm = SolarRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
773 |
+
|
774 |
+
def forward(
|
775 |
+
self,
|
776 |
+
hidden_states: torch.Tensor,
|
777 |
+
attention_mask: Optional[torch.Tensor] = None,
|
778 |
+
position_ids: Optional[torch.LongTensor] = None,
|
779 |
+
past_key_value: Optional[Cache] = None,
|
780 |
+
output_attentions: Optional[bool] = False,
|
781 |
+
use_cache: Optional[bool] = False,
|
782 |
+
cache_position: Optional[torch.LongTensor] = None,
|
783 |
+
**kwargs,
|
784 |
+
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
785 |
+
"""
|
786 |
+
Args:
|
787 |
+
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
|
788 |
+
attention_mask (`torch.FloatTensor`, *optional*):
|
789 |
+
attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
|
790 |
+
query_sequence_length, key_sequence_length)` if default attention is used.
|
791 |
+
output_attentions (`bool`, *optional*):
|
792 |
+
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
793 |
+
returned tensors for more detail.
|
794 |
+
use_cache (`bool`, *optional*):
|
795 |
+
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
|
796 |
+
(see `past_key_values`).
|
797 |
+
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
|
798 |
+
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
|
799 |
+
Indices depicting the position of the input sequence tokens in the sequence
|
800 |
+
kwargs (`dict`, *optional*):
|
801 |
+
Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
|
802 |
+
into the model
|
803 |
+
"""
|
804 |
+
residual = hidden_states
|
805 |
+
|
806 |
+
hidden_states = self.input_layernorm(hidden_states)
|
807 |
+
|
808 |
+
# Self Attention
|
809 |
+
hidden_states, self_attn_weights, present_key_value = self.self_attn(
|
810 |
+
hidden_states=hidden_states,
|
811 |
+
attention_mask=attention_mask,
|
812 |
+
position_ids=position_ids,
|
813 |
+
past_key_value=past_key_value,
|
814 |
+
output_attentions=output_attentions,
|
815 |
+
use_cache=use_cache,
|
816 |
+
cache_position=cache_position,
|
817 |
+
)
|
818 |
+
hidden_states = residual + hidden_states
|
819 |
+
|
820 |
+
# Fully Connected
|
821 |
+
residual = hidden_states
|
822 |
+
hidden_states = self.post_attention_layernorm(hidden_states)
|
823 |
+
hidden_states = self.mlp(hidden_states)
|
824 |
+
hidden_states = residual + hidden_states
|
825 |
+
|
826 |
+
outputs = (hidden_states,)
|
827 |
+
|
828 |
+
if output_attentions:
|
829 |
+
outputs += (self_attn_weights,)
|
830 |
+
|
831 |
+
if use_cache:
|
832 |
+
outputs += (present_key_value,)
|
833 |
+
|
834 |
+
return outputs
|
835 |
+
|
836 |
+
|
837 |
+
SOLAR_START_DOCSTRING = r"""
|
838 |
+
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
839 |
+
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
|
840 |
+
etc.)
|
841 |
+
|
842 |
+
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
843 |
+
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
844 |
+
and behavior.
|
845 |
+
|
846 |
+
Parameters:
|
847 |
+
config ([`SolarConfig`]):
|
848 |
+
Model configuration class with all the parameters of the model. Initializing with a config file does not
|
849 |
+
load the weights associated with the model, only the configuration. Check out the
|
850 |
+
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
851 |
+
"""
|
852 |
+
|
853 |
+
|
854 |
+
@add_start_docstrings(
|
855 |
+
"The bare Solar Model outputting raw hidden-states without any specific head on top.",
|
856 |
+
SOLAR_START_DOCSTRING,
|
857 |
+
)
|
858 |
+
class SolarPreTrainedModel(PreTrainedModel):
|
859 |
+
config_class = SolarConfig
|
860 |
+
base_model_prefix = "model"
|
861 |
+
supports_gradient_checkpointing = True
|
862 |
+
_no_split_modules = ["SolarDecoderLayer"]
|
863 |
+
_skip_keys_device_placement = ["past_key_values"]
|
864 |
+
_supports_flash_attn_2 = True
|
865 |
+
_supports_sdpa = True
|
866 |
+
_supports_cache_class = True
|
867 |
+
_supports_quantized_cache = True
|
868 |
+
_supports_static_cache = True
|
869 |
+
|
870 |
+
def _init_weights(self, module):
|
871 |
+
std = self.config.initializer_range
|
872 |
+
if isinstance(module, nn.Linear):
|
873 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
874 |
+
if module.bias is not None:
|
875 |
+
module.bias.data.zero_()
|
876 |
+
elif isinstance(module, nn.Embedding):
|
877 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
878 |
+
if module.padding_idx is not None:
|
879 |
+
module.weight.data[module.padding_idx].zero_()
|
880 |
+
|
881 |
+
|
882 |
+
SOLAR_INPUTS_DOCSTRING = r"""
|
883 |
+
Args:
|
884 |
+
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
885 |
+
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
|
886 |
+
it.
|
887 |
+
|
888 |
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
889 |
+
[`PreTrainedTokenizer.__call__`] for details.
|
890 |
+
|
891 |
+
[What are input IDs?](../glossary#input-ids)
|
892 |
+
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
893 |
+
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
894 |
+
|
895 |
+
- 1 for tokens that are **not masked**,
|
896 |
+
- 0 for tokens that are **masked**.
|
897 |
+
|
898 |
+
[What are attention masks?](../glossary#attention-mask)
|
899 |
+
|
900 |
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
901 |
+
[`PreTrainedTokenizer.__call__`] for details.
|
902 |
+
|
903 |
+
If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
|
904 |
+
`past_key_values`).
|
905 |
+
|
906 |
+
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
|
907 |
+
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
|
908 |
+
information on the default strategy.
|
909 |
+
|
910 |
+
- 1 indicates the head is **not masked**,
|
911 |
+
- 0 indicates the head is **masked**.
|
912 |
+
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
913 |
+
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
914 |
+
config.n_positions - 1]`.
|
915 |
+
|
916 |
+
[What are position IDs?](../glossary#position-ids)
|
917 |
+
past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
|
918 |
+
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
|
919 |
+
blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
|
920 |
+
returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
|
921 |
+
|
922 |
+
Two formats are allowed:
|
923 |
+
- a [`~cache_utils.Cache`] instance;
|
924 |
+
- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
|
925 |
+
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
|
926 |
+
cache format.
|
927 |
+
|
928 |
+
The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
|
929 |
+
legacy cache format will be returned.
|
930 |
+
|
931 |
+
If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
|
932 |
+
have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
|
933 |
+
of shape `(batch_size, sequence_length)`.
|
934 |
+
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
935 |
+
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
|
936 |
+
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
|
937 |
+
model's internal embedding lookup matrix.
|
938 |
+
use_cache (`bool`, *optional*):
|
939 |
+
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
|
940 |
+
`past_key_values`).
|
941 |
+
output_attentions (`bool`, *optional*):
|
942 |
+
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
943 |
+
tensors for more detail.
|
944 |
+
output_hidden_states (`bool`, *optional*):
|
945 |
+
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
946 |
+
more detail.
|
947 |
+
return_dict (`bool`, *optional*):
|
948 |
+
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
949 |
+
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
|
950 |
+
Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
|
951 |
+
this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
|
952 |
+
the complete sequence length.
|
953 |
+
"""
|
954 |
+
|
955 |
+
|
956 |
+
@add_start_docstrings(
|
957 |
+
"The bare Solar Model outputting raw hidden-states without any specific head on top.",
|
958 |
+
SOLAR_START_DOCSTRING,
|
959 |
+
)
|
960 |
+
class SolarModel(SolarPreTrainedModel):
|
961 |
+
"""
|
962 |
+
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`SolarDecoderLayer`]
|
963 |
+
|
964 |
+
Args:
|
965 |
+
config: SolarConfig
|
966 |
+
"""
|
967 |
+
|
968 |
+
def __init__(self, config: SolarConfig):
|
969 |
+
super().__init__(config)
|
970 |
+
self.padding_idx = config.pad_token_id
|
971 |
+
self.vocab_size = config.vocab_size
|
972 |
+
|
973 |
+
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
|
974 |
+
self.layers = nn.ModuleList(
|
975 |
+
[SolarDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
976 |
+
)
|
977 |
+
self._attn_implementation = config._attn_implementation
|
978 |
+
self.norm = SolarRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
979 |
+
|
980 |
+
self.gradient_checkpointing = False
|
981 |
+
# Initialize weights and apply final processing
|
982 |
+
self.post_init()
|
983 |
+
|
984 |
+
def get_input_embeddings(self):
|
985 |
+
return self.embed_tokens
|
986 |
+
|
987 |
+
def set_input_embeddings(self, value):
|
988 |
+
self.embed_tokens = value
|
989 |
+
|
990 |
+
@add_start_docstrings_to_model_forward(SOLAR_INPUTS_DOCSTRING)
|
991 |
+
def forward(
|
992 |
+
self,
|
993 |
+
input_ids: torch.LongTensor = None,
|
994 |
+
attention_mask: Optional[torch.Tensor] = None,
|
995 |
+
position_ids: Optional[torch.LongTensor] = None,
|
996 |
+
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
|
997 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
998 |
+
use_cache: Optional[bool] = None,
|
999 |
+
output_attentions: Optional[bool] = None,
|
1000 |
+
output_hidden_states: Optional[bool] = None,
|
1001 |
+
return_dict: Optional[bool] = None,
|
1002 |
+
cache_position: Optional[torch.LongTensor] = None,
|
1003 |
+
) -> Union[Tuple, BaseModelOutputWithPast]:
|
1004 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
1005 |
+
output_hidden_states = (
|
1006 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
1007 |
+
)
|
1008 |
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
1009 |
+
|
1010 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
1011 |
+
|
1012 |
+
# retrieve input_ids and inputs_embeds
|
1013 |
+
if (input_ids is None) ^ (inputs_embeds is not None):
|
1014 |
+
raise ValueError(
|
1015 |
+
"You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
|
1016 |
+
)
|
1017 |
+
|
1018 |
+
if self.gradient_checkpointing and self.training and use_cache:
|
1019 |
+
logger.warning_once(
|
1020 |
+
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
1021 |
+
)
|
1022 |
+
use_cache = False
|
1023 |
+
|
1024 |
+
if inputs_embeds is None:
|
1025 |
+
inputs_embeds = self.embed_tokens(input_ids)
|
1026 |
+
|
1027 |
+
return_legacy_cache = False
|
1028 |
+
if use_cache and not isinstance(past_key_values, Cache):
|
1029 |
+
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
|
1030 |
+
return_legacy_cache = True
|
1031 |
+
logger.warning_once(
|
1032 |
+
"We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. "
|
1033 |
+
"Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)"
|
1034 |
+
)
|
1035 |
+
|
1036 |
+
if cache_position is None:
|
1037 |
+
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
1038 |
+
cache_position = torch.arange(
|
1039 |
+
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
|
1040 |
+
)
|
1041 |
+
|
1042 |
+
if position_ids is None:
|
1043 |
+
position_ids = cache_position.unsqueeze(0)
|
1044 |
+
|
1045 |
+
causal_mask = self._update_causal_mask(
|
1046 |
+
attention_mask, inputs_embeds, cache_position, past_key_values, use_cache, output_attentions
|
1047 |
+
)
|
1048 |
+
|
1049 |
+
hidden_states = inputs_embeds
|
1050 |
+
|
1051 |
+
# decoder layers
|
1052 |
+
all_hidden_states = () if output_hidden_states else None
|
1053 |
+
all_self_attns = () if output_attentions else None
|
1054 |
+
next_decoder_cache = None
|
1055 |
+
|
1056 |
+
bskcn_1 = None
|
1057 |
+
bskcn_2 = None
|
1058 |
+
bskcn_tv = self.config.bskcn_tv[0] if self.training else self.config.bskcn_tv[1]
|
1059 |
+
for layer_idx, decoder_layer in enumerate(self.layers):
|
1060 |
+
if layer_idx in self.config.bskcn_1:
|
1061 |
+
bskcn_1 = hidden_states
|
1062 |
+
if layer_idx in self.config.bskcn_2:
|
1063 |
+
bskcn_2 = hidden_states
|
1064 |
+
if layer_idx in self.config.bskcn_3:
|
1065 |
+
hidden_states = (bskcn_1*bskcn_tv).to(hidden_states.device) + hidden_states*(1-bskcn_tv)
|
1066 |
+
if layer_idx in self.config.bskcn_4:
|
1067 |
+
hidden_states = (bskcn_2*bskcn_tv).to(hidden_states.device) + hidden_states*(1-bskcn_tv)
|
1068 |
+
|
1069 |
+
if output_hidden_states:
|
1070 |
+
all_hidden_states += (hidden_states,)
|
1071 |
+
|
1072 |
+
if self.gradient_checkpointing and self.training:
|
1073 |
+
layer_outputs = self._gradient_checkpointing_func(
|
1074 |
+
decoder_layer.__call__,
|
1075 |
+
hidden_states,
|
1076 |
+
causal_mask,
|
1077 |
+
position_ids,
|
1078 |
+
past_key_values,
|
1079 |
+
output_attentions,
|
1080 |
+
use_cache,
|
1081 |
+
cache_position,
|
1082 |
+
)
|
1083 |
+
else:
|
1084 |
+
layer_outputs = decoder_layer(
|
1085 |
+
hidden_states,
|
1086 |
+
attention_mask=causal_mask,
|
1087 |
+
position_ids=position_ids,
|
1088 |
+
past_key_value=past_key_values,
|
1089 |
+
output_attentions=output_attentions,
|
1090 |
+
use_cache=use_cache,
|
1091 |
+
cache_position=cache_position,
|
1092 |
+
)
|
1093 |
+
|
1094 |
+
hidden_states = layer_outputs[0]
|
1095 |
+
|
1096 |
+
if use_cache:
|
1097 |
+
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
|
1098 |
+
|
1099 |
+
if output_attentions:
|
1100 |
+
all_self_attns += (layer_outputs[1],)
|
1101 |
+
|
1102 |
+
hidden_states = self.norm(hidden_states)
|
1103 |
+
|
1104 |
+
# add hidden states from the last decoder layer
|
1105 |
+
if output_hidden_states:
|
1106 |
+
all_hidden_states += (hidden_states,)
|
1107 |
+
|
1108 |
+
next_cache = next_decoder_cache if use_cache else None
|
1109 |
+
if return_legacy_cache:
|
1110 |
+
next_cache = next_cache.to_legacy_cache()
|
1111 |
+
|
1112 |
+
if not return_dict:
|
1113 |
+
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
|
1114 |
+
return BaseModelOutputWithPast(
|
1115 |
+
last_hidden_state=hidden_states,
|
1116 |
+
past_key_values=next_cache,
|
1117 |
+
hidden_states=all_hidden_states,
|
1118 |
+
attentions=all_self_attns,
|
1119 |
+
)
|
1120 |
+
|
1121 |
+
def _update_causal_mask(
|
1122 |
+
self,
|
1123 |
+
attention_mask: torch.Tensor,
|
1124 |
+
input_tensor: torch.Tensor,
|
1125 |
+
cache_position: torch.Tensor,
|
1126 |
+
past_key_values: Cache,
|
1127 |
+
use_cache: bool,
|
1128 |
+
output_attentions: bool,
|
1129 |
+
):
|
1130 |
+
# TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static
|
1131 |
+
# KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes.
|
1132 |
+
# (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
|
1133 |
+
# `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114
|
1134 |
+
|
1135 |
+
if self._attn_implementation == "flash_attention_2":
|
1136 |
+
if attention_mask is not None and use_cache:
|
1137 |
+
is_padding_right = attention_mask[:, -1].sum().item() != input_tensor.size()[0]
|
1138 |
+
if is_padding_right:
|
1139 |
+
raise ValueError(
|
1140 |
+
"You are attempting to perform batched generation with padding_side='right'"
|
1141 |
+
" this may lead to unexpected behaviour for Flash Attention version of Solar. Make sure to "
|
1142 |
+
" call `tokenizer.padding_side = 'left'` before tokenizing the input. "
|
1143 |
+
)
|
1144 |
+
if attention_mask is not None and 0.0 in attention_mask:
|
1145 |
+
return attention_mask
|
1146 |
+
return None
|
1147 |
+
|
1148 |
+
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
|
1149 |
+
# order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
|
1150 |
+
# to infer the attention mask.
|
1151 |
+
|
1152 |
+
# cache_position must be valid here no matter which cache we use
|
1153 |
+
past_seen_tokens = cache_position[0] if past_key_values is not None else 0
|
1154 |
+
using_static_cache = isinstance(past_key_values, StaticCache)
|
1155 |
+
using_sliding_window_cache = isinstance(past_key_values, SlidingWindowCache)
|
1156 |
+
|
1157 |
+
if (
|
1158 |
+
self.config._attn_implementation == "sdpa"
|
1159 |
+
and not (using_static_cache or using_sliding_window_cache)
|
1160 |
+
and not output_attentions
|
1161 |
+
):
|
1162 |
+
if AttentionMaskConverter._ignore_causal_mask_sdpa(
|
1163 |
+
attention_mask,
|
1164 |
+
inputs_embeds=input_tensor,
|
1165 |
+
past_key_values_length=past_seen_tokens,
|
1166 |
+
sliding_window=self.config.sliding_window,
|
1167 |
+
is_training=self.training,
|
1168 |
+
):
|
1169 |
+
return None
|
1170 |
+
|
1171 |
+
dtype, device = input_tensor.dtype, input_tensor.device
|
1172 |
+
min_dtype = torch.finfo(dtype).min
|
1173 |
+
sequence_length = input_tensor.shape[1]
|
1174 |
+
# SlidingWindowCache
|
1175 |
+
if using_sliding_window_cache:
|
1176 |
+
target_length = max(sequence_length, self.config.sliding_window)
|
1177 |
+
# StaticCache
|
1178 |
+
elif using_static_cache:
|
1179 |
+
target_length = past_key_values.get_max_length()
|
1180 |
+
# DynamicCache or no cache
|
1181 |
+
else:
|
1182 |
+
target_length = (
|
1183 |
+
attention_mask.shape[-1]
|
1184 |
+
if isinstance(attention_mask, torch.Tensor)
|
1185 |
+
else past_seen_tokens + sequence_length + 1
|
1186 |
+
)
|
1187 |
+
|
1188 |
+
if attention_mask is not None and attention_mask.dim() == 4:
|
1189 |
+
# in this case we assume that the mask comes already in inverted form and requires no inversion or slicing
|
1190 |
+
if attention_mask.max() != 0:
|
1191 |
+
raise ValueError("Custom 4D attention mask should be passed in inverted form with max==0`")
|
1192 |
+
causal_mask = attention_mask
|
1193 |
+
else:
|
1194 |
+
causal_mask = torch.full(
|
1195 |
+
(sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
|
1196 |
+
)
|
1197 |
+
exclude_mask = torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
|
1198 |
+
if self.config.sliding_window is not None:
|
1199 |
+
if not using_sliding_window_cache or sequence_length > self.config.sliding_window:
|
1200 |
+
exclude_mask |= torch.arange(target_length, device=device) <= (
|
1201 |
+
cache_position.reshape(-1, 1) - self.config.sliding_window
|
1202 |
+
)
|
1203 |
+
causal_mask *= exclude_mask
|
1204 |
+
causal_mask = causal_mask[None, None, :, :].expand(input_tensor.shape[0], 1, -1, -1)
|
1205 |
+
if attention_mask is not None:
|
1206 |
+
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
|
1207 |
+
if attention_mask.dim() == 2:
|
1208 |
+
mask_length = attention_mask.shape[-1]
|
1209 |
+
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
|
1210 |
+
padding_mask = padding_mask == 0
|
1211 |
+
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
|
1212 |
+
padding_mask, min_dtype
|
1213 |
+
)
|
1214 |
+
|
1215 |
+
if (
|
1216 |
+
self.config._attn_implementation == "sdpa"
|
1217 |
+
and attention_mask is not None
|
1218 |
+
and attention_mask.device.type == "cuda"
|
1219 |
+
and not output_attentions
|
1220 |
+
):
|
1221 |
+
# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
|
1222 |
+
# using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
|
1223 |
+
# Details: https://github.com/pytorch/pytorch/issues/110213
|
1224 |
+
causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
|
1225 |
+
|
1226 |
+
return causal_mask
|
1227 |
+
|
1228 |
+
# Copied from transformers.models.mistral.modeling_mistal.SolarCasualLM
|
1229 |
+
class SolarForCausalLM(SolarPreTrainedModel):
|
1230 |
+
_tied_weights_keys = ["lm_head.weight"]
|
1231 |
+
|
1232 |
+
def __init__(self, config):
|
1233 |
+
super().__init__(config)
|
1234 |
+
self.model = SolarModel(config)
|
1235 |
+
self.vocab_size = config.vocab_size
|
1236 |
+
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
1237 |
+
|
1238 |
+
# Initialize weights and apply final processing
|
1239 |
+
self.post_init()
|
1240 |
+
|
1241 |
+
def get_input_embeddings(self):
|
1242 |
+
return self.model.embed_tokens
|
1243 |
+
|
1244 |
+
def set_input_embeddings(self, value):
|
1245 |
+
self.model.embed_tokens = value
|
1246 |
+
|
1247 |
+
def get_output_embeddings(self):
|
1248 |
+
return self.lm_head
|
1249 |
+
|
1250 |
+
def set_output_embeddings(self, new_embeddings):
|
1251 |
+
self.lm_head = new_embeddings
|
1252 |
+
|
1253 |
+
def set_decoder(self, decoder):
|
1254 |
+
self.model = decoder
|
1255 |
+
|
1256 |
+
def get_decoder(self):
|
1257 |
+
return self.model
|
1258 |
+
|
1259 |
+
@add_start_docstrings_to_model_forward(SOLAR_INPUTS_DOCSTRING)
|
1260 |
+
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
|
1261 |
+
def forward(
|
1262 |
+
self,
|
1263 |
+
input_ids: torch.LongTensor = None,
|
1264 |
+
attention_mask: Optional[torch.Tensor] = None,
|
1265 |
+
position_ids: Optional[torch.LongTensor] = None,
|
1266 |
+
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
|
1267 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
1268 |
+
labels: Optional[torch.LongTensor] = None,
|
1269 |
+
use_cache: Optional[bool] = None,
|
1270 |
+
output_attentions: Optional[bool] = None,
|
1271 |
+
output_hidden_states: Optional[bool] = None,
|
1272 |
+
return_dict: Optional[bool] = None,
|
1273 |
+
cache_position: Optional[torch.LongTensor] = None,
|
1274 |
+
) -> Union[Tuple, CausalLMOutputWithPast]:
|
1275 |
+
r"""
|
1276 |
+
Args:
|
1277 |
+
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
1278 |
+
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
1279 |
+
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
1280 |
+
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
1281 |
+
|
1282 |
+
Returns:
|
1283 |
+
|
1284 |
+
Example:
|
1285 |
+
|
1286 |
+
```python
|
1287 |
+
>>> from transformers import AutoTokenizer, SolarForCausalLM
|
1288 |
+
|
1289 |
+
>>> model = SolarForCausalLM.from_pretrained("upstage/Solar-pro-1.0")
|
1290 |
+
>>> tokenizer = AutoTokenizer.from_pretrained("upstage/Solar-pro-1.0")
|
1291 |
+
|
1292 |
+
>>> prompt = "Hey, are you conscious? Can you talk to me?"
|
1293 |
+
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
1294 |
+
|
1295 |
+
>>> # Generate
|
1296 |
+
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
1297 |
+
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
1298 |
+
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
|
1299 |
+
```"""
|
1300 |
+
|
1301 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
1302 |
+
output_hidden_states = (
|
1303 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
1304 |
+
)
|
1305 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
1306 |
+
|
1307 |
+
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
1308 |
+
outputs = self.model(
|
1309 |
+
input_ids=input_ids,
|
1310 |
+
attention_mask=attention_mask,
|
1311 |
+
position_ids=position_ids,
|
1312 |
+
past_key_values=past_key_values,
|
1313 |
+
inputs_embeds=inputs_embeds,
|
1314 |
+
use_cache=use_cache,
|
1315 |
+
output_attentions=output_attentions,
|
1316 |
+
output_hidden_states=output_hidden_states,
|
1317 |
+
return_dict=return_dict,
|
1318 |
+
cache_position=cache_position,
|
1319 |
+
)
|
1320 |
+
|
1321 |
+
hidden_states = outputs[0]
|
1322 |
+
logits = self.lm_head(hidden_states)
|
1323 |
+
logits = logits.float()
|
1324 |
+
|
1325 |
+
loss = None
|
1326 |
+
if labels is not None:
|
1327 |
+
# Shift so that tokens < n predict n
|
1328 |
+
shift_logits = logits[..., :-1, :].contiguous()
|
1329 |
+
shift_labels = labels[..., 1:].contiguous()
|
1330 |
+
# Flatten the tokens
|
1331 |
+
shift_logits = shift_logits.view(-1, self.config.vocab_size)
|
1332 |
+
shift_labels = shift_labels.view(-1)
|
1333 |
+
# Ensure tensors are on the same device
|
1334 |
+
shift_labels = shift_labels.to(shift_logits.device)
|
1335 |
+
loss_fct = CrossEntropyLoss()
|
1336 |
+
loss = loss_fct(shift_logits, shift_labels)
|
1337 |
+
|
1338 |
+
if not return_dict:
|
1339 |
+
output = (logits,) + outputs[1:]
|
1340 |
+
return (loss,) + output if loss is not None else output
|
1341 |
+
|
1342 |
+
return CausalLMOutputWithPast(
|
1343 |
+
loss=loss,
|
1344 |
+
logits=logits,
|
1345 |
+
past_key_values=outputs.past_key_values,
|
1346 |
+
hidden_states=outputs.hidden_states,
|
1347 |
+
attentions=outputs.attentions,
|
1348 |
+
)
|
1349 |
+
|
1350 |
+
def prepare_inputs_for_generation(
|
1351 |
+
self,
|
1352 |
+
input_ids,
|
1353 |
+
past_key_values=None,
|
1354 |
+
attention_mask=None,
|
1355 |
+
inputs_embeds=None,
|
1356 |
+
cache_position=None,
|
1357 |
+
use_cache=True,
|
1358 |
+
**kwargs,
|
1359 |
+
):
|
1360 |
+
past_length = 0
|
1361 |
+
# Omit tokens covered by past_key_values
|
1362 |
+
if past_key_values is not None:
|
1363 |
+
# Past key values are always initialized with a `Cache` object -> no need for if-else anymore
|
1364 |
+
past_length = cache_position[0] if cache_position is not None else past_key_values.get_seq_length()
|
1365 |
+
max_cache_length = (
|
1366 |
+
torch.tensor(past_key_values.get_max_length(), device=input_ids.device)
|
1367 |
+
if past_key_values.get_max_length() is not None
|
1368 |
+
else None
|
1369 |
+
)
|
1370 |
+
cache_length = past_length if max_cache_length is None else torch.min(max_cache_length, past_length)
|
1371 |
+
|
1372 |
+
# Keep only the unprocessed tokens:
|
1373 |
+
# 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
|
1374 |
+
# some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
|
1375 |
+
# input)
|
1376 |
+
if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
|
1377 |
+
input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
|
1378 |
+
# 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
|
1379 |
+
# input_ids based on the past_length.
|
1380 |
+
elif past_length < input_ids.shape[1]:
|
1381 |
+
input_ids = input_ids[:, past_length:]
|
1382 |
+
# 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
|
1383 |
+
|
1384 |
+
# If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
|
1385 |
+
if (
|
1386 |
+
max_cache_length is not None
|
1387 |
+
and attention_mask is not None
|
1388 |
+
and cache_length + input_ids.shape[1] > max_cache_length
|
1389 |
+
):
|
1390 |
+
attention_mask = attention_mask[:, -max_cache_length:]
|
1391 |
+
|
1392 |
+
position_ids = kwargs.get("position_ids", None)
|
1393 |
+
if attention_mask is not None and position_ids is None:
|
1394 |
+
# create position_ids on the fly for batch generation
|
1395 |
+
position_ids = attention_mask.long().cumsum(-1) - 1
|
1396 |
+
position_ids.masked_fill_(attention_mask == 0, 1)
|
1397 |
+
if past_key_values:
|
1398 |
+
position_ids = position_ids[:, -input_ids.shape[1] :]
|
1399 |
+
|
1400 |
+
# crop the attention_mask to sliding window size during decode phase if using SlidingWindowCache
|
1401 |
+
if (
|
1402 |
+
past_length > 0
|
1403 |
+
and attention_mask is not None
|
1404 |
+
and isinstance(past_key_values, SlidingWindowCache)
|
1405 |
+
and attention_mask.shape[1] > past_key_values.max_cache_len
|
1406 |
+
):
|
1407 |
+
attention_mask = attention_mask[:, -past_key_values.max_cache_len :]
|
1408 |
+
|
1409 |
+
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
1410 |
+
if inputs_embeds is not None and past_length == 0:
|
1411 |
+
model_inputs = {"inputs_embeds": inputs_embeds}
|
1412 |
+
else:
|
1413 |
+
model_inputs = {"input_ids": input_ids.contiguous()}
|
1414 |
+
|
1415 |
+
input_length = position_ids.shape[-1] if position_ids is not None else input_ids.shape[-1]
|
1416 |
+
if cache_position is None:
|
1417 |
+
cache_position = torch.arange(past_length, past_length + input_length, device=input_ids.device)
|
1418 |
+
elif use_cache:
|
1419 |
+
cache_position = cache_position[-input_length:]
|
1420 |
+
|
1421 |
+
model_inputs.update(
|
1422 |
+
{
|
1423 |
+
"position_ids": position_ids,
|
1424 |
+
"cache_position": cache_position,
|
1425 |
+
"past_key_values": past_key_values,
|
1426 |
+
"use_cache": use_cache,
|
1427 |
+
"attention_mask": attention_mask,
|
1428 |
+
}
|
1429 |
+
)
|
1430 |
+
return model_inputs
|
1431 |
+
|
1432 |
+
@staticmethod
|
1433 |
+
def _reorder_cache(past_key_values, beam_idx):
|
1434 |
+
reordered_past = ()
|
1435 |
+
for layer_past in past_key_values:
|
1436 |
+
reordered_past += (
|
1437 |
+
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
|
1438 |
+
)
|
1439 |
+
return reordered_past
|
1440 |
+
|
1441 |
+
|
1442 |
+
@add_start_docstrings(
|
1443 |
+
"""
|
1444 |
+
The Solar Model transformer with a sequence classification head on top (linear layer).
|
1445 |
+
|
1446 |
+
[`SolarForSequenceClassification`] uses the last token in order to do the classification, as other causal models
|
1447 |
+
(e.g. GPT-2) do.
|
1448 |
+
|
1449 |
+
Since it does classification on the last token, it requires to know the position of the last token. If a
|
1450 |
+
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
|
1451 |
+
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
|
1452 |
+
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
|
1453 |
+
each row of the batch).
|
1454 |
+
""",
|
1455 |
+
SOLAR_START_DOCSTRING,
|
1456 |
+
)
|
1457 |
+
class SolarForSequenceClassification(SolarPreTrainedModel):
|
1458 |
+
def __init__(self, config):
|
1459 |
+
super().__init__(config)
|
1460 |
+
self.num_labels = config.num_labels
|
1461 |
+
self.model = SolarModel(config)
|
1462 |
+
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
|
1463 |
+
|
1464 |
+
# Initialize weights and apply final processing
|
1465 |
+
self.post_init()
|
1466 |
+
|
1467 |
+
def get_input_embeddings(self):
|
1468 |
+
return self.model.embed_tokens
|
1469 |
+
|
1470 |
+
def set_input_embeddings(self, value):
|
1471 |
+
self.model.embed_tokens = value
|
1472 |
+
|
1473 |
+
@add_start_docstrings_to_model_forward(SOLAR_INPUTS_DOCSTRING)
|
1474 |
+
def forward(
|
1475 |
+
self,
|
1476 |
+
input_ids: torch.LongTensor = None,
|
1477 |
+
attention_mask: Optional[torch.Tensor] = None,
|
1478 |
+
position_ids: Optional[torch.LongTensor] = None,
|
1479 |
+
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
|
1480 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
1481 |
+
labels: Optional[torch.LongTensor] = None,
|
1482 |
+
use_cache: Optional[bool] = None,
|
1483 |
+
output_attentions: Optional[bool] = None,
|
1484 |
+
output_hidden_states: Optional[bool] = None,
|
1485 |
+
return_dict: Optional[bool] = None,
|
1486 |
+
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
|
1487 |
+
r"""
|
1488 |
+
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
1489 |
+
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
1490 |
+
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
1491 |
+
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
1492 |
+
"""
|
1493 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
1494 |
+
|
1495 |
+
transformer_outputs = self.model(
|
1496 |
+
input_ids,
|
1497 |
+
attention_mask=attention_mask,
|
1498 |
+
position_ids=position_ids,
|
1499 |
+
past_key_values=past_key_values,
|
1500 |
+
inputs_embeds=inputs_embeds,
|
1501 |
+
use_cache=use_cache,
|
1502 |
+
output_attentions=output_attentions,
|
1503 |
+
output_hidden_states=output_hidden_states,
|
1504 |
+
return_dict=return_dict,
|
1505 |
+
)
|
1506 |
+
hidden_states = transformer_outputs[0]
|
1507 |
+
logits = self.score(hidden_states)
|
1508 |
+
|
1509 |
+
if input_ids is not None:
|
1510 |
+
batch_size = input_ids.shape[0]
|
1511 |
+
else:
|
1512 |
+
batch_size = inputs_embeds.shape[0]
|
1513 |
+
|
1514 |
+
if self.config.pad_token_id is None and batch_size != 1:
|
1515 |
+
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
|
1516 |
+
if self.config.pad_token_id is None:
|
1517 |
+
sequence_lengths = -1
|
1518 |
+
else:
|
1519 |
+
if input_ids is not None:
|
1520 |
+
# if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
|
1521 |
+
sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
|
1522 |
+
sequence_lengths = sequence_lengths % input_ids.shape[-1]
|
1523 |
+
sequence_lengths = sequence_lengths.to(logits.device)
|
1524 |
+
else:
|
1525 |
+
sequence_lengths = -1
|
1526 |
+
|
1527 |
+
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
|
1528 |
+
|
1529 |
+
loss = None
|
1530 |
+
if labels is not None:
|
1531 |
+
labels = labels.to(logits.device)
|
1532 |
+
if self.config.problem_type is None:
|
1533 |
+
if self.num_labels == 1:
|
1534 |
+
self.config.problem_type = "regression"
|
1535 |
+
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
1536 |
+
self.config.problem_type = "single_label_classification"
|
1537 |
+
else:
|
1538 |
+
self.config.problem_type = "multi_label_classification"
|
1539 |
+
|
1540 |
+
if self.config.problem_type == "regression":
|
1541 |
+
loss_fct = MSELoss()
|
1542 |
+
if self.num_labels == 1:
|
1543 |
+
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
|
1544 |
+
else:
|
1545 |
+
loss = loss_fct(pooled_logits, labels)
|
1546 |
+
elif self.config.problem_type == "single_label_classification":
|
1547 |
+
loss_fct = CrossEntropyLoss()
|
1548 |
+
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
|
1549 |
+
elif self.config.problem_type == "multi_label_classification":
|
1550 |
+
loss_fct = BCEWithLogitsLoss()
|
1551 |
+
loss = loss_fct(pooled_logits, labels)
|
1552 |
+
if not return_dict:
|
1553 |
+
output = (pooled_logits,) + transformer_outputs[1:]
|
1554 |
+
return ((loss,) + output) if loss is not None else output
|
1555 |
+
|
1556 |
+
return SequenceClassifierOutputWithPast(
|
1557 |
+
loss=loss,
|
1558 |
+
logits=pooled_logits,
|
1559 |
+
past_key_values=transformer_outputs.past_key_values,
|
1560 |
+
hidden_states=transformer_outputs.hidden_states,
|
1561 |
+
attentions=transformer_outputs.attentions,
|
1562 |
+
)
|
1563 |
+
|
1564 |
+
|
1565 |
+
@add_start_docstrings(
|
1566 |
+
"""
|
1567 |
+
The Solar Model transformer with a span classification head on top for extractive question-answering tasks like
|
1568 |
+
SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
|
1569 |
+
""",
|
1570 |
+
SOLAR_START_DOCSTRING,
|
1571 |
+
)
|
1572 |
+
class SolarForQuestionAnswering(SolarPreTrainedModel):
|
1573 |
+
base_model_prefix = "transformer"
|
1574 |
+
|
1575 |
+
# Copied from transformers.models.bloom.modeling_bloom.BloomForQuestionAnswering.__init__ with Bloom->Solar
|
1576 |
+
def __init__(self, config):
|
1577 |
+
super().__init__(config)
|
1578 |
+
self.transformer = SolarModel(config)
|
1579 |
+
self.qa_outputs = nn.Linear(config.hidden_size, 2)
|
1580 |
+
|
1581 |
+
# Initialize weights and apply final processing
|
1582 |
+
self.post_init()
|
1583 |
+
|
1584 |
+
def get_input_embeddings(self):
|
1585 |
+
return self.transformer.embed_tokens
|
1586 |
+
|
1587 |
+
def set_input_embeddings(self, value):
|
1588 |
+
self.transformer.embed_tokens = value
|
1589 |
+
|
1590 |
+
@add_start_docstrings_to_model_forward(SOLAR_INPUTS_DOCSTRING)
|
1591 |
+
def forward(
|
1592 |
+
self,
|
1593 |
+
input_ids: Optional[torch.LongTensor] = None,
|
1594 |
+
attention_mask: Optional[torch.FloatTensor] = None,
|
1595 |
+
position_ids: Optional[torch.LongTensor] = None,
|
1596 |
+
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
|
1597 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
1598 |
+
start_positions: Optional[torch.LongTensor] = None,
|
1599 |
+
end_positions: Optional[torch.LongTensor] = None,
|
1600 |
+
output_attentions: Optional[bool] = None,
|
1601 |
+
output_hidden_states: Optional[bool] = None,
|
1602 |
+
return_dict: Optional[bool] = None,
|
1603 |
+
) -> Union[Tuple, QuestionAnsweringModelOutput]:
|
1604 |
+
r"""
|
1605 |
+
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
1606 |
+
Labels for position (index) of the start of the labelled span for computing the token classification loss.
|
1607 |
+
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
1608 |
+
are not taken into account for computing the loss.
|
1609 |
+
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
1610 |
+
Labels for position (index) of the end of the labelled span for computing the token classification loss.
|
1611 |
+
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
1612 |
+
are not taken into account for computing the loss.
|
1613 |
+
"""
|
1614 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
1615 |
+
|
1616 |
+
outputs = self.transformer(
|
1617 |
+
input_ids,
|
1618 |
+
attention_mask=attention_mask,
|
1619 |
+
position_ids=position_ids,
|
1620 |
+
past_key_values=past_key_values,
|
1621 |
+
inputs_embeds=inputs_embeds,
|
1622 |
+
output_attentions=output_attentions,
|
1623 |
+
output_hidden_states=output_hidden_states,
|
1624 |
+
return_dict=return_dict,
|
1625 |
+
)
|
1626 |
+
|
1627 |
+
sequence_output = outputs[0]
|
1628 |
+
|
1629 |
+
logits = self.qa_outputs(sequence_output)
|
1630 |
+
start_logits, end_logits = logits.split(1, dim=-1)
|
1631 |
+
start_logits = start_logits.squeeze(-1).contiguous()
|
1632 |
+
end_logits = end_logits.squeeze(-1).contiguous()
|
1633 |
+
|
1634 |
+
total_loss = None
|
1635 |
+
if start_positions is not None and end_positions is not None:
|
1636 |
+
# If we are on multi-GPU, split add a dimension
|
1637 |
+
if len(start_positions.size()) > 1:
|
1638 |
+
start_positions = start_positions.squeeze(-1).to(start_logits.device)
|
1639 |
+
if len(end_positions.size()) > 1:
|
1640 |
+
end_positions = end_positions.squeeze(-1).to(end_logits.device)
|
1641 |
+
# sometimes the start/end positions are outside our model inputs, we ignore these terms
|
1642 |
+
ignored_index = start_logits.size(1)
|
1643 |
+
start_positions = start_positions.clamp(0, ignored_index)
|
1644 |
+
end_positions = end_positions.clamp(0, ignored_index)
|
1645 |
+
|
1646 |
+
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
|
1647 |
+
start_loss = loss_fct(start_logits, start_positions)
|
1648 |
+
end_loss = loss_fct(end_logits, end_positions)
|
1649 |
+
total_loss = (start_loss + end_loss) / 2
|
1650 |
+
|
1651 |
+
if not return_dict:
|
1652 |
+
output = (start_logits, end_logits) + outputs[2:]
|
1653 |
+
return ((total_loss,) + output) if total_loss is not None else output
|
1654 |
+
|
1655 |
+
return QuestionAnsweringModelOutput(
|
1656 |
+
loss=total_loss,
|
1657 |
+
start_logits=start_logits,
|
1658 |
+
end_logits=end_logits,
|
1659 |
+
hidden_states=outputs.hidden_states,
|
1660 |
+
attentions=outputs.attentions,
|
1661 |
+
)
|
1662 |
+
|
1663 |
+
|
1664 |
+
@add_start_docstrings(
|
1665 |
+
"""
|
1666 |
+
The Solar Model transformer with a token classification head on top (a linear layer on top of the hidden-states
|
1667 |
+
output) e.g. for Named-Entity-Recognition (NER) tasks.
|
1668 |
+
""",
|
1669 |
+
SOLAR_START_DOCSTRING,
|
1670 |
+
)
|
1671 |
+
class SolarForTokenClassification(SolarPreTrainedModel):
|
1672 |
+
def __init__(self, config):
|
1673 |
+
super().__init__(config)
|
1674 |
+
self.num_labels = config.num_labels
|
1675 |
+
self.model = SolarModel(config)
|
1676 |
+
if getattr(config, "classifier_dropout", None) is not None:
|
1677 |
+
classifier_dropout = config.classifier_dropout
|
1678 |
+
elif getattr(config, "hidden_dropout", None) is not None:
|
1679 |
+
classifier_dropout = config.hidden_dropout
|
1680 |
+
else:
|
1681 |
+
classifier_dropout = 0.1
|
1682 |
+
self.dropout = nn.Dropout(classifier_dropout)
|
1683 |
+
self.score = nn.Linear(config.hidden_size, config.num_labels)
|
1684 |
+
|
1685 |
+
# Initialize weights and apply final processing
|
1686 |
+
self.post_init()
|
1687 |
+
|
1688 |
+
def get_input_embeddings(self):
|
1689 |
+
return self.model.embed_tokens
|
1690 |
+
|
1691 |
+
def set_input_embeddings(self, value):
|
1692 |
+
self.model.embed_tokens = value
|
1693 |
+
|
1694 |
+
@add_start_docstrings_to_model_forward(SOLAR_INPUTS_DOCSTRING)
|
1695 |
+
def forward(
|
1696 |
+
self,
|
1697 |
+
input_ids: Optional[torch.LongTensor] = None,
|
1698 |
+
attention_mask: Optional[torch.Tensor] = None,
|
1699 |
+
position_ids: Optional[torch.LongTensor] = None,
|
1700 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
1701 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
1702 |
+
labels: Optional[torch.LongTensor] = None,
|
1703 |
+
use_cache: Optional[bool] = None,
|
1704 |
+
output_attentions: Optional[bool] = None,
|
1705 |
+
output_hidden_states: Optional[bool] = None,
|
1706 |
+
return_dict: Optional[bool] = None,
|
1707 |
+
) -> Union[Tuple, TokenClassifierOutput]:
|
1708 |
+
r"""
|
1709 |
+
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
1710 |
+
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
1711 |
+
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
1712 |
+
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
1713 |
+
"""
|
1714 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
1715 |
+
|
1716 |
+
outputs = self.model(
|
1717 |
+
input_ids,
|
1718 |
+
attention_mask=attention_mask,
|
1719 |
+
position_ids=position_ids,
|
1720 |
+
past_key_values=past_key_values,
|
1721 |
+
inputs_embeds=inputs_embeds,
|
1722 |
+
use_cache=use_cache,
|
1723 |
+
output_attentions=output_attentions,
|
1724 |
+
output_hidden_states=output_hidden_states,
|
1725 |
+
return_dict=return_dict,
|
1726 |
+
)
|
1727 |
+
sequence_output = outputs[0]
|
1728 |
+
sequence_output = self.dropout(sequence_output)
|
1729 |
+
logits = self.score(sequence_output)
|
1730 |
+
|
1731 |
+
loss = None
|
1732 |
+
if labels is not None:
|
1733 |
+
loss_fct = CrossEntropyLoss()
|
1734 |
+
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
1735 |
+
|
1736 |
+
if not return_dict:
|
1737 |
+
output = (logits,) + outputs[2:]
|
1738 |
+
return ((loss,) + output) if loss is not None else output
|
1739 |
+
|
1740 |
+
return TokenClassifierOutput(
|
1741 |
+
loss=loss,
|
1742 |
+
logits=logits,
|
1743 |
+
hidden_states=outputs.hidden_states,
|
1744 |
+
attentions=outputs.attentions,
|
1745 |
+
)
|
solar-pro-banner.png
ADDED
![]() |
special_tokens_map.json
ADDED
@@ -0,0 +1,30 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"bos_token": {
|
3 |
+
"content": "<|startoftext|>",
|
4 |
+
"lstrip": false,
|
5 |
+
"normalized": false,
|
6 |
+
"rstrip": false,
|
7 |
+
"single_word": false
|
8 |
+
},
|
9 |
+
"eos_token": {
|
10 |
+
"content": "<|im_end|>",
|
11 |
+
"lstrip": false,
|
12 |
+
"normalized": false,
|
13 |
+
"rstrip": false,
|
14 |
+
"single_word": false
|
15 |
+
},
|
16 |
+
"pad_token": {
|
17 |
+
"content": "<|im_end|>",
|
18 |
+
"lstrip": false,
|
19 |
+
"normalized": false,
|
20 |
+
"rstrip": false,
|
21 |
+
"single_word": false
|
22 |
+
},
|
23 |
+
"unk_token": {
|
24 |
+
"content": "<unk>",
|
25 |
+
"lstrip": false,
|
26 |
+
"normalized": false,
|
27 |
+
"rstrip": false,
|
28 |
+
"single_word": false
|
29 |
+
}
|
30 |
+
}
|
tokenizer.json
ADDED
The diff for this file is too large to render.
See raw diff
|
|
tokenizer.model
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:c241ca72f5e6b8ea5dba3cc3eeb37fcbee6e41ef1f28debd0251b03a901c1918
|
3 |
+
size 131
|
tokenizer_config.json
ADDED
@@ -0,0 +1,1067 @@
|
|
|
|
|
|
|
|
|
|
|
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"32093": {
|
775 |
+
"content": "<|placeholder89|>",
|
776 |
+
"lstrip": false,
|
777 |
+
"normalized": false,
|
778 |
+
"rstrip": true,
|
779 |
+
"single_word": false,
|
780 |
+
"special": true
|
781 |
+
},
|
782 |
+
"32094": {
|
783 |
+
"content": "<|placeholder90|>",
|
784 |
+
"lstrip": false,
|
785 |
+
"normalized": false,
|
786 |
+
"rstrip": true,
|
787 |
+
"single_word": false,
|
788 |
+
"special": true
|
789 |
+
},
|
790 |
+
"32095": {
|
791 |
+
"content": "<|placeholder91|>",
|
792 |
+
"lstrip": false,
|
793 |
+
"normalized": false,
|
794 |
+
"rstrip": true,
|
795 |
+
"single_word": false,
|
796 |
+
"special": true
|
797 |
+
},
|
798 |
+
"32096": {
|
799 |
+
"content": "<|placeholder92|>",
|
800 |
+
"lstrip": false,
|
801 |
+
"normalized": false,
|
802 |
+
"rstrip": true,
|
803 |
+
"single_word": false,
|
804 |
+
"special": true
|
805 |
+
},
|
806 |
+
"32097": {
|
807 |
+
"content": "<|placeholder93|>",
|
808 |
+
"lstrip": false,
|
809 |
+
"normalized": false,
|
810 |
+
"rstrip": true,
|
811 |
+
"single_word": false,
|
812 |
+
"special": true
|
813 |
+
},
|
814 |
+
"32098": {
|
815 |
+
"content": "<|placeholder94|>",
|
816 |
+
"lstrip": false,
|
817 |
+
"normalized": false,
|
818 |
+
"rstrip": true,
|
819 |
+
"single_word": false,
|
820 |
+
"special": true
|
821 |
+
},
|
822 |
+
"32099": {
|
823 |
+
"content": "<|placeholder95|>",
|
824 |
+
"lstrip": false,
|
825 |
+
"normalized": false,
|
826 |
+
"rstrip": true,
|
827 |
+
"single_word": false,
|
828 |
+
"special": true
|
829 |
+
},
|
830 |
+
"32100": {
|
831 |
+
"content": "<|placeholder96|>",
|
832 |
+
"lstrip": false,
|
833 |
+
"normalized": false,
|
834 |
+
"rstrip": true,
|
835 |
+
"single_word": false,
|
836 |
+
"special": true
|
837 |
+
},
|
838 |
+
"32101": {
|
839 |
+
"content": "<|placeholder97|>",
|
840 |
+
"lstrip": false,
|
841 |
+
"normalized": false,
|
842 |
+
"rstrip": true,
|
843 |
+
"single_word": false,
|
844 |
+
"special": true
|
845 |
+
},
|
846 |
+
"32102": {
|
847 |
+
"content": "<|placeholder98|>",
|
848 |
+
"lstrip": false,
|
849 |
+
"normalized": false,
|
850 |
+
"rstrip": true,
|
851 |
+
"single_word": false,
|
852 |
+
"special": true
|
853 |
+
},
|
854 |
+
"32103": {
|
855 |
+
"content": "<|placeholder99|>",
|
856 |
+
"lstrip": false,
|
857 |
+
"normalized": false,
|
858 |
+
"rstrip": true,
|
859 |
+
"single_word": false,
|
860 |
+
"special": true
|
861 |
+
},
|
862 |
+
"32104": {
|
863 |
+
"content": "<|placeholder100|>",
|
864 |
+
"lstrip": false,
|
865 |
+
"normalized": false,
|
866 |
+
"rstrip": true,
|
867 |
+
"single_word": false,
|
868 |
+
"special": true
|
869 |
+
},
|
870 |
+
"32105": {
|
871 |
+
"content": "<|placeholder101|>",
|
872 |
+
"lstrip": false,
|
873 |
+
"normalized": false,
|
874 |
+
"rstrip": true,
|
875 |
+
"single_word": false,
|
876 |
+
"special": true
|
877 |
+
},
|
878 |
+
"32106": {
|
879 |
+
"content": "<|placeholder102|>",
|
880 |
+
"lstrip": false,
|
881 |
+
"normalized": false,
|
882 |
+
"rstrip": true,
|
883 |
+
"single_word": false,
|
884 |
+
"special": true
|
885 |
+
},
|
886 |
+
"32107": {
|
887 |
+
"content": "<|placeholder103|>",
|
888 |
+
"lstrip": false,
|
889 |
+
"normalized": false,
|
890 |
+
"rstrip": true,
|
891 |
+
"single_word": false,
|
892 |
+
"special": true
|
893 |
+
},
|
894 |
+
"32108": {
|
895 |
+
"content": "<|placeholder104|>",
|
896 |
+
"lstrip": false,
|
897 |
+
"normalized": false,
|
898 |
+
"rstrip": true,
|
899 |
+
"single_word": false,
|
900 |
+
"special": true
|
901 |
+
},
|
902 |
+
"32109": {
|
903 |
+
"content": "<|placeholder105|>",
|
904 |
+
"lstrip": false,
|
905 |
+
"normalized": false,
|
906 |
+
"rstrip": true,
|
907 |
+
"single_word": false,
|
908 |
+
"special": true
|
909 |
+
},
|
910 |
+
"32110": {
|
911 |
+
"content": "<|placeholder106|>",
|
912 |
+
"lstrip": false,
|
913 |
+
"normalized": false,
|
914 |
+
"rstrip": true,
|
915 |
+
"single_word": false,
|
916 |
+
"special": true
|
917 |
+
},
|
918 |
+
"32111": {
|
919 |
+
"content": "<|placeholder107|>",
|
920 |
+
"lstrip": false,
|
921 |
+
"normalized": false,
|
922 |
+
"rstrip": true,
|
923 |
+
"single_word": false,
|
924 |
+
"special": true
|
925 |
+
},
|
926 |
+
"32112": {
|
927 |
+
"content": "<|placeholder108|>",
|
928 |
+
"lstrip": false,
|
929 |
+
"normalized": false,
|
930 |
+
"rstrip": true,
|
931 |
+
"single_word": false,
|
932 |
+
"special": true
|
933 |
+
},
|
934 |
+
"32113": {
|
935 |
+
"content": "<|placeholder109|>",
|
936 |
+
"lstrip": false,
|
937 |
+
"normalized": false,
|
938 |
+
"rstrip": true,
|
939 |
+
"single_word": false,
|
940 |
+
"special": true
|
941 |
+
},
|
942 |
+
"32114": {
|
943 |
+
"content": "<|placeholder110|>",
|
944 |
+
"lstrip": false,
|
945 |
+
"normalized": false,
|
946 |
+
"rstrip": true,
|
947 |
+
"single_word": false,
|
948 |
+
"special": true
|
949 |
+
},
|
950 |
+
"32115": {
|
951 |
+
"content": "<|placeholder111|>",
|
952 |
+
"lstrip": false,
|
953 |
+
"normalized": false,
|
954 |
+
"rstrip": true,
|
955 |
+
"single_word": false,
|
956 |
+
"special": true
|
957 |
+
},
|
958 |
+
"32116": {
|
959 |
+
"content": "<|placeholder112|>",
|
960 |
+
"lstrip": false,
|
961 |
+
"normalized": false,
|
962 |
+
"rstrip": true,
|
963 |
+
"single_word": false,
|
964 |
+
"special": true
|
965 |
+
},
|
966 |
+
"32117": {
|
967 |
+
"content": "<|placeholder113|>",
|
968 |
+
"lstrip": false,
|
969 |
+
"normalized": false,
|
970 |
+
"rstrip": true,
|
971 |
+
"single_word": false,
|
972 |
+
"special": true
|
973 |
+
},
|
974 |
+
"32118": {
|
975 |
+
"content": "<|placeholder114|>",
|
976 |
+
"lstrip": false,
|
977 |
+
"normalized": false,
|
978 |
+
"rstrip": true,
|
979 |
+
"single_word": false,
|
980 |
+
"special": true
|
981 |
+
},
|
982 |
+
"32119": {
|
983 |
+
"content": "<|placeholder115|>",
|
984 |
+
"lstrip": false,
|
985 |
+
"normalized": false,
|
986 |
+
"rstrip": true,
|
987 |
+
"single_word": false,
|
988 |
+
"special": true
|
989 |
+
},
|
990 |
+
"32120": {
|
991 |
+
"content": "<|placeholder116|>",
|
992 |
+
"lstrip": false,
|
993 |
+
"normalized": false,
|
994 |
+
"rstrip": true,
|
995 |
+
"single_word": false,
|
996 |
+
"special": true
|
997 |
+
},
|
998 |
+
"32121": {
|
999 |
+
"content": "<|placeholder117|>",
|
1000 |
+
"lstrip": false,
|
1001 |
+
"normalized": false,
|
1002 |
+
"rstrip": true,
|
1003 |
+
"single_word": false,
|
1004 |
+
"special": true
|
1005 |
+
},
|
1006 |
+
"32122": {
|
1007 |
+
"content": "<|placeholder118|>",
|
1008 |
+
"lstrip": false,
|
1009 |
+
"normalized": false,
|
1010 |
+
"rstrip": true,
|
1011 |
+
"single_word": false,
|
1012 |
+
"special": true
|
1013 |
+
},
|
1014 |
+
"32123": {
|
1015 |
+
"content": "<|placeholder119|>",
|
1016 |
+
"lstrip": false,
|
1017 |
+
"normalized": false,
|
1018 |
+
"rstrip": true,
|
1019 |
+
"single_word": false,
|
1020 |
+
"special": true
|
1021 |
+
},
|
1022 |
+
"32124": {
|
1023 |
+
"content": "<|placeholder120|>",
|
1024 |
+
"lstrip": false,
|
1025 |
+
"normalized": false,
|
1026 |
+
"rstrip": true,
|
1027 |
+
"single_word": false,
|
1028 |
+
"special": true
|
1029 |
+
},
|
1030 |
+
"32125": {
|
1031 |
+
"content": "<|placeholder121|>",
|
1032 |
+
"lstrip": false,
|
1033 |
+
"normalized": false,
|
1034 |
+
"rstrip": true,
|
1035 |
+
"single_word": false,
|
1036 |
+
"special": true
|
1037 |
+
},
|
1038 |
+
"32126": {
|
1039 |
+
"content": "<|placeholder122|>",
|
1040 |
+
"lstrip": false,
|
1041 |
+
"normalized": false,
|
1042 |
+
"rstrip": true,
|
1043 |
+
"single_word": false,
|
1044 |
+
"special": true
|
1045 |
+
},
|
1046 |
+
"32127": {
|
1047 |
+
"content": "<|placeholder123|>",
|
1048 |
+
"lstrip": false,
|
1049 |
+
"normalized": false,
|
1050 |
+
"rstrip": true,
|
1051 |
+
"single_word": false,
|
1052 |
+
"special": true
|
1053 |
+
}
|
1054 |
+
},
|
1055 |
+
"bos_token": "<|startoftext|>",
|
1056 |
+
"chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
|
1057 |
+
"clean_up_tokenization_spaces": false,
|
1058 |
+
"eos_token": "<|im_end|>",
|
1059 |
+
"legacy": true,
|
1060 |
+
"model_max_length": 4096,
|
1061 |
+
"pad_token": "<|im_end|>",
|
1062 |
+
"padding_side": "left",
|
1063 |
+
"sp_model_kwargs": {},
|
1064 |
+
"tokenizer_class": "LlamaTokenizer",
|
1065 |
+
"unk_token": "<unk>",
|
1066 |
+
"use_default_system_prompt": false
|
1067 |
+
}
|
vllm_solar.py
ADDED
@@ -0,0 +1,552 @@
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|
1 |
+
# coding=utf-8
|
2 |
+
# Adapted from
|
3 |
+
# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
|
4 |
+
# Copyright 2023 The vLLM team.
|
5 |
+
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
|
6 |
+
#
|
7 |
+
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
8 |
+
# and OPT implementations in this library. It has been modified from its
|
9 |
+
# original forms to accommodate minor architectural differences compared
|
10 |
+
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
11 |
+
#
|
12 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
13 |
+
# you may not use this file except in compliance with the License.
|
14 |
+
# You may obtain a copy of the License at
|
15 |
+
#
|
16 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
17 |
+
#
|
18 |
+
# Unless required by applicable law or agreed to in writing, software
|
19 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
20 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
21 |
+
# See the License for the specific language governing permissions and
|
22 |
+
# limitations under the License.
|
23 |
+
"""Inference-only Solar model compatible with HuggingFace weights."""
|
24 |
+
from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
|
25 |
+
|
26 |
+
import torch
|
27 |
+
from torch import nn
|
28 |
+
|
29 |
+
from vllm.attention import Attention, AttentionMetadata
|
30 |
+
from vllm.config import CacheConfig, LoRAConfig
|
31 |
+
from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
|
32 |
+
get_tensor_model_parallel_world_size)
|
33 |
+
from vllm.model_executor.layers.activation import SiluAndMul
|
34 |
+
from vllm.model_executor.layers.layernorm import RMSNorm
|
35 |
+
from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
|
36 |
+
QKVParallelLinear,
|
37 |
+
RowParallelLinear)
|
38 |
+
from vllm.model_executor.layers.logits_processor import LogitsProcessor
|
39 |
+
from vllm.model_executor.layers.quantization.base_config import (
|
40 |
+
QuantizationConfig)
|
41 |
+
from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
|
42 |
+
get_compressed_tensors_cache_scale)
|
43 |
+
from vllm.model_executor.layers.rotary_embedding import get_rope
|
44 |
+
from vllm.model_executor.layers.sampler import Sampler
|
45 |
+
from vllm.model_executor.layers.vocab_parallel_embedding import (
|
46 |
+
DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
|
47 |
+
from vllm.model_executor.model_loader.weight_utils import (
|
48 |
+
default_weight_loader, kv_cache_scales_loader, maybe_remap_kv_scale_name)
|
49 |
+
from vllm.model_executor.sampling_metadata import SamplingMetadata
|
50 |
+
from vllm.sequence import IntermediateTensors, SamplerOutput
|
51 |
+
from vllm.utils import is_hip
|
52 |
+
|
53 |
+
from vllm.model_executor.models.interfaces import SupportsLoRA
|
54 |
+
from vllm.model_executor.models.utils import PPMissingLayer, is_pp_missing_parameter, make_layers
|
55 |
+
|
56 |
+
class SolarMLP(nn.Module):
|
57 |
+
|
58 |
+
def __init__(
|
59 |
+
self,
|
60 |
+
hidden_size: int,
|
61 |
+
intermediate_size: int,
|
62 |
+
hidden_act: str,
|
63 |
+
quant_config: Optional[QuantizationConfig] = None,
|
64 |
+
bias: bool = False,
|
65 |
+
prefix: str = "",
|
66 |
+
) -> None:
|
67 |
+
super().__init__()
|
68 |
+
self.gate_up_proj = MergedColumnParallelLinear(
|
69 |
+
input_size=hidden_size,
|
70 |
+
output_sizes=[intermediate_size] * 2,
|
71 |
+
bias=bias,
|
72 |
+
quant_config=quant_config,
|
73 |
+
prefix=f"{prefix}.gate_up_proj")
|
74 |
+
self.down_proj = RowParallelLinear(input_size=intermediate_size,
|
75 |
+
output_size=hidden_size,
|
76 |
+
bias=bias,
|
77 |
+
quant_config=quant_config,
|
78 |
+
prefix=f"{prefix}.down_proj")
|
79 |
+
if hidden_act != "silu":
|
80 |
+
raise ValueError(f"Unsupported activation: {hidden_act}. "
|
81 |
+
"Only silu is supported for now.")
|
82 |
+
self.act_fn = SiluAndMul()
|
83 |
+
|
84 |
+
def forward(self, x):
|
85 |
+
gate_up, _ = self.gate_up_proj(x)
|
86 |
+
x = self.act_fn(gate_up)
|
87 |
+
x, _ = self.down_proj(x)
|
88 |
+
return x
|
89 |
+
|
90 |
+
|
91 |
+
class SolarAttention(nn.Module):
|
92 |
+
|
93 |
+
def __init__(
|
94 |
+
self,
|
95 |
+
config,
|
96 |
+
hidden_size: int,
|
97 |
+
num_heads: int,
|
98 |
+
num_kv_heads: int,
|
99 |
+
rope_theta: float = 10000,
|
100 |
+
rope_scaling: Optional[Dict[str, Any]] = None,
|
101 |
+
max_position_embeddings: int = 8192,
|
102 |
+
quant_config: Optional[QuantizationConfig] = None,
|
103 |
+
bias: bool = False,
|
104 |
+
cache_config: Optional[CacheConfig] = None,
|
105 |
+
prefix: str = "",
|
106 |
+
) -> None:
|
107 |
+
super().__init__()
|
108 |
+
self.hidden_size = hidden_size
|
109 |
+
tp_size = get_tensor_model_parallel_world_size()
|
110 |
+
self.total_num_heads = num_heads
|
111 |
+
assert self.total_num_heads % tp_size == 0
|
112 |
+
self.num_heads = self.total_num_heads // tp_size
|
113 |
+
self.total_num_kv_heads = num_kv_heads
|
114 |
+
if self.total_num_kv_heads >= tp_size:
|
115 |
+
# Number of KV heads is greater than TP size, so we partition
|
116 |
+
# the KV heads across multiple tensor parallel GPUs.
|
117 |
+
assert self.total_num_kv_heads % tp_size == 0
|
118 |
+
else:
|
119 |
+
# Number of KV heads is less than TP size, so we replicate
|
120 |
+
# the KV heads across multiple tensor parallel GPUs.
|
121 |
+
assert tp_size % self.total_num_kv_heads == 0
|
122 |
+
self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
|
123 |
+
# MistralConfig has an optional head_dim introduced by Mistral-Nemo
|
124 |
+
self.head_dim = getattr(config, "head_dim",
|
125 |
+
self.hidden_size // self.total_num_heads)
|
126 |
+
self.q_size = self.num_heads * self.head_dim
|
127 |
+
self.kv_size = self.num_kv_heads * self.head_dim
|
128 |
+
self.scaling = self.head_dim**-0.5
|
129 |
+
self.rope_theta = rope_theta
|
130 |
+
self.max_position_embeddings = max_position_embeddings
|
131 |
+
|
132 |
+
self.qkv_proj = QKVParallelLinear(
|
133 |
+
hidden_size=hidden_size,
|
134 |
+
head_size=self.head_dim,
|
135 |
+
total_num_heads=self.total_num_heads,
|
136 |
+
total_num_kv_heads=self.total_num_kv_heads,
|
137 |
+
bias=bias,
|
138 |
+
quant_config=quant_config,
|
139 |
+
prefix=f"{prefix}.qkv_proj",
|
140 |
+
)
|
141 |
+
self.o_proj = RowParallelLinear(
|
142 |
+
input_size=self.total_num_heads * self.head_dim,
|
143 |
+
output_size=hidden_size,
|
144 |
+
bias=bias,
|
145 |
+
quant_config=quant_config,
|
146 |
+
prefix=f"{prefix}.o_proj",
|
147 |
+
)
|
148 |
+
|
149 |
+
self.rotary_emb = get_rope(
|
150 |
+
self.head_dim,
|
151 |
+
rotary_dim=self.head_dim,
|
152 |
+
max_position=max_position_embeddings,
|
153 |
+
base=rope_theta,
|
154 |
+
rope_scaling=rope_scaling,
|
155 |
+
)
|
156 |
+
self.attn = Attention(self.num_heads,
|
157 |
+
self.head_dim,
|
158 |
+
self.scaling,
|
159 |
+
num_kv_heads=self.num_kv_heads,
|
160 |
+
cache_config=cache_config,
|
161 |
+
quant_config=quant_config)
|
162 |
+
|
163 |
+
def forward(
|
164 |
+
self,
|
165 |
+
positions: torch.Tensor,
|
166 |
+
hidden_states: torch.Tensor,
|
167 |
+
kv_cache: torch.Tensor,
|
168 |
+
attn_metadata: AttentionMetadata,
|
169 |
+
) -> torch.Tensor:
|
170 |
+
qkv, _ = self.qkv_proj(hidden_states)
|
171 |
+
q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
|
172 |
+
q, k = self.rotary_emb(positions, q, k)
|
173 |
+
attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
|
174 |
+
output, _ = self.o_proj(attn_output)
|
175 |
+
return output
|
176 |
+
|
177 |
+
|
178 |
+
class SolarDecoderLayer(nn.Module):
|
179 |
+
|
180 |
+
def __init__(
|
181 |
+
self,
|
182 |
+
config,
|
183 |
+
cache_config: Optional[CacheConfig] = None,
|
184 |
+
quant_config: Optional[QuantizationConfig] = None,
|
185 |
+
prefix: str = "",
|
186 |
+
) -> None:
|
187 |
+
super().__init__()
|
188 |
+
self.hidden_size = config.hidden_size
|
189 |
+
rope_theta = getattr(config, "rope_theta", 10000)
|
190 |
+
rope_scaling = getattr(config, "rope_scaling", None)
|
191 |
+
if rope_scaling is not None and getattr(
|
192 |
+
config, "original_max_position_embeddings", None):
|
193 |
+
rope_scaling["original_max_position_embeddings"] = (
|
194 |
+
config.original_max_position_embeddings)
|
195 |
+
max_position_embeddings = getattr(config, "max_position_embeddings",
|
196 |
+
8192)
|
197 |
+
# Support abacusai/Smaug-72B-v0.1 with attention_bias
|
198 |
+
# Support internlm/internlm-7b with bias
|
199 |
+
attention_bias = getattr(config, "attention_bias", False) or getattr(
|
200 |
+
config, "bias", False)
|
201 |
+
self.self_attn = SolarAttention(
|
202 |
+
config=config,
|
203 |
+
hidden_size=self.hidden_size,
|
204 |
+
num_heads=config.num_attention_heads,
|
205 |
+
num_kv_heads=getattr(config, "num_key_value_heads",
|
206 |
+
config.num_attention_heads),
|
207 |
+
rope_theta=rope_theta,
|
208 |
+
rope_scaling=rope_scaling,
|
209 |
+
max_position_embeddings=max_position_embeddings,
|
210 |
+
quant_config=quant_config,
|
211 |
+
bias=attention_bias,
|
212 |
+
cache_config=cache_config,
|
213 |
+
prefix=f"{prefix}.self_attn",
|
214 |
+
)
|
215 |
+
self.mlp = SolarMLP(
|
216 |
+
hidden_size=self.hidden_size,
|
217 |
+
intermediate_size=config.intermediate_size,
|
218 |
+
hidden_act=config.hidden_act,
|
219 |
+
quant_config=quant_config,
|
220 |
+
bias=getattr(config, "mlp_bias", False),
|
221 |
+
prefix=f"{prefix}.mlp",
|
222 |
+
)
|
223 |
+
self.input_layernorm = RMSNorm(config.hidden_size,
|
224 |
+
eps=config.rms_norm_eps)
|
225 |
+
self.post_attention_layernorm = RMSNorm(config.hidden_size,
|
226 |
+
eps=config.rms_norm_eps)
|
227 |
+
|
228 |
+
def forward(
|
229 |
+
self,
|
230 |
+
positions: torch.Tensor,
|
231 |
+
hidden_states: torch.Tensor,
|
232 |
+
kv_cache: torch.Tensor,
|
233 |
+
attn_metadata: AttentionMetadata,
|
234 |
+
residual: Optional[torch.Tensor],
|
235 |
+
) -> Tuple[torch.Tensor, torch.Tensor]:
|
236 |
+
# Self Attention
|
237 |
+
if residual is None:
|
238 |
+
residual = hidden_states
|
239 |
+
hidden_states = self.input_layernorm(hidden_states)
|
240 |
+
else:
|
241 |
+
hidden_states, residual = self.input_layernorm(
|
242 |
+
hidden_states, residual)
|
243 |
+
hidden_states = self.self_attn(
|
244 |
+
positions=positions,
|
245 |
+
hidden_states=hidden_states,
|
246 |
+
kv_cache=kv_cache,
|
247 |
+
attn_metadata=attn_metadata,
|
248 |
+
)
|
249 |
+
|
250 |
+
# Fully Connected
|
251 |
+
hidden_states, residual = self.post_attention_layernorm(
|
252 |
+
hidden_states, residual)
|
253 |
+
hidden_states = self.mlp(hidden_states)
|
254 |
+
return hidden_states, residual
|
255 |
+
|
256 |
+
|
257 |
+
class SolarModel(nn.Module):
|
258 |
+
|
259 |
+
def __init__(
|
260 |
+
self,
|
261 |
+
config,
|
262 |
+
cache_config: Optional[CacheConfig] = None,
|
263 |
+
quant_config: Optional[QuantizationConfig] = None,
|
264 |
+
lora_config: Optional[LoRAConfig] = None,
|
265 |
+
prefix: str = "",
|
266 |
+
) -> None:
|
267 |
+
super().__init__()
|
268 |
+
self.config = config
|
269 |
+
self.padding_idx = config.pad_token_id
|
270 |
+
lora_vocab = (lora_config.lora_extra_vocab_size *
|
271 |
+
(lora_config.max_loras or 1)) if lora_config else 0
|
272 |
+
self.vocab_size = config.vocab_size + lora_vocab
|
273 |
+
self.org_vocab_size = config.vocab_size
|
274 |
+
if get_pp_group().is_first_rank or (config.tie_word_embeddings
|
275 |
+
and get_pp_group().is_last_rank):
|
276 |
+
self.embed_tokens = VocabParallelEmbedding(
|
277 |
+
self.vocab_size,
|
278 |
+
config.hidden_size,
|
279 |
+
org_num_embeddings=config.vocab_size,
|
280 |
+
)
|
281 |
+
else:
|
282 |
+
self.embed_tokens = PPMissingLayer()
|
283 |
+
self.start_layer, self.end_layer, self.layers = make_layers(
|
284 |
+
config.num_hidden_layers,
|
285 |
+
lambda prefix: SolarDecoderLayer(config=config,
|
286 |
+
cache_config=cache_config,
|
287 |
+
quant_config=quant_config,
|
288 |
+
prefix=prefix),
|
289 |
+
prefix=f"{prefix}.layers")
|
290 |
+
if get_pp_group().is_last_rank:
|
291 |
+
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
292 |
+
else:
|
293 |
+
self.norm = PPMissingLayer()
|
294 |
+
|
295 |
+
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
|
296 |
+
return self.embed_tokens(input_ids)
|
297 |
+
|
298 |
+
def forward(
|
299 |
+
self,
|
300 |
+
input_ids: Optional[torch.Tensor],
|
301 |
+
positions: torch.Tensor,
|
302 |
+
kv_caches: List[torch.Tensor],
|
303 |
+
attn_metadata: AttentionMetadata,
|
304 |
+
intermediate_tensors: Optional[IntermediateTensors],
|
305 |
+
inputs_embeds: Optional[torch.Tensor] = None,
|
306 |
+
) -> Union[torch.Tensor, IntermediateTensors]:
|
307 |
+
if get_pp_group().is_first_rank:
|
308 |
+
if inputs_embeds is not None:
|
309 |
+
hidden_states = inputs_embeds
|
310 |
+
else:
|
311 |
+
hidden_states = self.get_input_embeddings(input_ids)
|
312 |
+
residual = None
|
313 |
+
else:
|
314 |
+
assert intermediate_tensors is not None
|
315 |
+
hidden_states = intermediate_tensors["hidden_states"]
|
316 |
+
residual = intermediate_tensors["residual"]
|
317 |
+
|
318 |
+
bskcn_h_1 = None
|
319 |
+
bskcn_h_2 = None
|
320 |
+
bskcn_r_1 = None
|
321 |
+
bskcn_r_2 = None
|
322 |
+
bskcn_tv = self.config.bskcn_tv[0] if self.training else self.config.bskcn_tv[1]
|
323 |
+
|
324 |
+
for i in range(self.start_layer, self.end_layer):
|
325 |
+
if i in self.config.bskcn_1:
|
326 |
+
bskcn_h_1 = hidden_states.clone()
|
327 |
+
bskcn_r_1 = residual.clone()
|
328 |
+
if i in self.config.bskcn_2:
|
329 |
+
bskcn_h_2 = hidden_states.clone()
|
330 |
+
bskcn_r_2 = residual.clone()
|
331 |
+
if i in self.config.bskcn_3:
|
332 |
+
hidden_states = bskcn_h_1*bskcn_tv + hidden_states*(1-bskcn_tv)
|
333 |
+
residual = bskcn_r_1*bskcn_tv + residual*(1-bskcn_tv)
|
334 |
+
if i in self.config.bskcn_4:
|
335 |
+
hidden_states = bskcn_h_2*bskcn_tv + hidden_states*(1-bskcn_tv)
|
336 |
+
residual = bskcn_r_2*bskcn_tv + residual*(1-bskcn_tv)
|
337 |
+
layer = self.layers[i]
|
338 |
+
hidden_states, residual = layer(
|
339 |
+
positions,
|
340 |
+
hidden_states,
|
341 |
+
kv_caches[i - self.start_layer],
|
342 |
+
attn_metadata,
|
343 |
+
residual,
|
344 |
+
)
|
345 |
+
|
346 |
+
if not get_pp_group().is_last_rank:
|
347 |
+
return IntermediateTensors({
|
348 |
+
"hidden_states": hidden_states,
|
349 |
+
"residual": residual
|
350 |
+
})
|
351 |
+
|
352 |
+
hidden_states, _ = self.norm(hidden_states, residual)
|
353 |
+
return hidden_states
|
354 |
+
|
355 |
+
|
356 |
+
class SolarForCausalLM(nn.Module, SupportsLoRA):
|
357 |
+
packed_modules_mapping = {
|
358 |
+
"qkv_proj": [
|
359 |
+
"q_proj",
|
360 |
+
"k_proj",
|
361 |
+
"v_proj",
|
362 |
+
],
|
363 |
+
"gate_up_proj": [
|
364 |
+
"gate_proj",
|
365 |
+
"up_proj",
|
366 |
+
],
|
367 |
+
}
|
368 |
+
|
369 |
+
# LoRA specific attributes
|
370 |
+
supported_lora_modules = [
|
371 |
+
"qkv_proj", "o_proj", "gate_up_proj", "down_proj", "embed_tokens",
|
372 |
+
"lm_head"
|
373 |
+
]
|
374 |
+
embedding_modules = {
|
375 |
+
"embed_tokens": "input_embeddings",
|
376 |
+
"lm_head": "output_embeddings",
|
377 |
+
}
|
378 |
+
embedding_padding_modules = ["lm_head"]
|
379 |
+
bitsandbytes_stacked_params_mapping = {
|
380 |
+
# shard_name, weight_name, index
|
381 |
+
"q_proj": ("qkv_proj", 0),
|
382 |
+
"k_proj": ("qkv_proj", 1),
|
383 |
+
"v_proj": ("qkv_proj", 2),
|
384 |
+
"gate_proj": ("gate_up_proj", 0),
|
385 |
+
"up_proj": ("gate_up_proj", 1),
|
386 |
+
}
|
387 |
+
|
388 |
+
def __init__(
|
389 |
+
self,
|
390 |
+
config,
|
391 |
+
cache_config: Optional[CacheConfig] = None,
|
392 |
+
quant_config: Optional[QuantizationConfig] = None,
|
393 |
+
lora_config: Optional[LoRAConfig] = None,
|
394 |
+
) -> None:
|
395 |
+
super().__init__()
|
396 |
+
|
397 |
+
self.config = config
|
398 |
+
self.lora_config = lora_config
|
399 |
+
|
400 |
+
self.model = SolarModel(config,
|
401 |
+
cache_config,
|
402 |
+
quant_config,
|
403 |
+
lora_config=lora_config,
|
404 |
+
prefix="model")
|
405 |
+
if get_pp_group().is_last_rank:
|
406 |
+
self.unpadded_vocab_size = config.vocab_size
|
407 |
+
if lora_config:
|
408 |
+
self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
|
409 |
+
self.lm_head = ParallelLMHead(
|
410 |
+
self.unpadded_vocab_size,
|
411 |
+
config.hidden_size,
|
412 |
+
org_num_embeddings=config.vocab_size,
|
413 |
+
padding_size=DEFAULT_VOCAB_PADDING_SIZE
|
414 |
+
# We need bigger padding if using lora for kernel
|
415 |
+
# compatibility
|
416 |
+
if not lora_config else lora_config.lora_vocab_padding_size,
|
417 |
+
quant_config=quant_config,
|
418 |
+
)
|
419 |
+
if config.tie_word_embeddings:
|
420 |
+
self.lm_head.weight = self.model.embed_tokens.weight
|
421 |
+
|
422 |
+
logit_scale = getattr(config, "logit_scale", 1.0)
|
423 |
+
self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
|
424 |
+
config.vocab_size,
|
425 |
+
logit_scale)
|
426 |
+
self.sampler = Sampler()
|
427 |
+
else:
|
428 |
+
self.lm_head = PPMissingLayer()
|
429 |
+
|
430 |
+
def forward(
|
431 |
+
self,
|
432 |
+
input_ids: torch.Tensor,
|
433 |
+
positions: torch.Tensor,
|
434 |
+
kv_caches: List[torch.Tensor],
|
435 |
+
attn_metadata: AttentionMetadata,
|
436 |
+
intermediate_tensors: Optional[IntermediateTensors] = None,
|
437 |
+
) -> Union[torch.Tensor, IntermediateTensors]:
|
438 |
+
model_output = self.model(input_ids, positions, kv_caches,
|
439 |
+
attn_metadata, intermediate_tensors)
|
440 |
+
return model_output
|
441 |
+
|
442 |
+
def compute_logits(self, hidden_states: torch.Tensor,
|
443 |
+
sampling_metadata: SamplingMetadata) -> torch.Tensor:
|
444 |
+
logits = self.logits_processor(self.lm_head, hidden_states,
|
445 |
+
sampling_metadata)
|
446 |
+
return logits
|
447 |
+
|
448 |
+
def sample(
|
449 |
+
self,
|
450 |
+
logits: torch.Tensor,
|
451 |
+
sampling_metadata: SamplingMetadata,
|
452 |
+
) -> Optional[SamplerOutput]:
|
453 |
+
next_tokens = self.sampler(logits, sampling_metadata)
|
454 |
+
return next_tokens
|
455 |
+
|
456 |
+
def make_empty_intermediate_tensors(
|
457 |
+
self, batch_size: int, dtype: torch.dtype,
|
458 |
+
device: torch.device) -> IntermediateTensors:
|
459 |
+
return IntermediateTensors({
|
460 |
+
"hidden_states":
|
461 |
+
torch.zeros((batch_size, self.config.hidden_size),
|
462 |
+
dtype=dtype,
|
463 |
+
device=device),
|
464 |
+
"residual":
|
465 |
+
torch.zeros((batch_size, self.config.hidden_size),
|
466 |
+
dtype=dtype,
|
467 |
+
device=device),
|
468 |
+
})
|
469 |
+
|
470 |
+
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
471 |
+
stacked_params_mapping = [
|
472 |
+
# (param_name, shard_name, shard_id)
|
473 |
+
(".qkv_proj", ".q_proj", "q"),
|
474 |
+
(".qkv_proj", ".k_proj", "k"),
|
475 |
+
(".qkv_proj", ".v_proj", "v"),
|
476 |
+
(".gate_up_proj", ".gate_proj", 0),
|
477 |
+
(".gate_up_proj", ".up_proj", 1),
|
478 |
+
]
|
479 |
+
params_dict = dict(self.named_parameters())
|
480 |
+
for name, loaded_weight in weights:
|
481 |
+
if "rotary_emb.inv_freq" in name:
|
482 |
+
continue
|
483 |
+
if ("rotary_emb.cos_cached" in name
|
484 |
+
or "rotary_emb.sin_cached" in name):
|
485 |
+
# Models trained using ColossalAI may include these tensors in
|
486 |
+
# the checkpoint. Skip them.
|
487 |
+
continue
|
488 |
+
if scale_name := get_compressed_tensors_cache_scale(name):
|
489 |
+
# Loading kv cache scales for compressed-tensors quantization
|
490 |
+
param = params_dict[scale_name]
|
491 |
+
weight_loader = getattr(param, "weight_loader",
|
492 |
+
default_weight_loader)
|
493 |
+
loaded_weight = loaded_weight[0]
|
494 |
+
weight_loader(param, loaded_weight)
|
495 |
+
continue
|
496 |
+
for (param_name, weight_name, shard_id) in stacked_params_mapping:
|
497 |
+
if weight_name not in name:
|
498 |
+
continue
|
499 |
+
name = name.replace(weight_name, param_name)
|
500 |
+
# Skip loading extra bias for GPTQ models.
|
501 |
+
if name.endswith(".bias") and name not in params_dict:
|
502 |
+
continue
|
503 |
+
|
504 |
+
if is_pp_missing_parameter(name, self):
|
505 |
+
continue
|
506 |
+
|
507 |
+
param = params_dict[name]
|
508 |
+
weight_loader = param.weight_loader
|
509 |
+
weight_loader(param, loaded_weight, shard_id)
|
510 |
+
|
511 |
+
break
|
512 |
+
else:
|
513 |
+
# Skip loading extra bias for GPTQ models.
|
514 |
+
if name.endswith(".bias") and name not in params_dict:
|
515 |
+
continue
|
516 |
+
# Remapping the name of FP8 kv-scale.
|
517 |
+
name = maybe_remap_kv_scale_name(name, params_dict)
|
518 |
+
if name is None:
|
519 |
+
continue
|
520 |
+
|
521 |
+
if is_pp_missing_parameter(name, self):
|
522 |
+
continue
|
523 |
+
|
524 |
+
param = params_dict[name]
|
525 |
+
weight_loader = getattr(param, "weight_loader",
|
526 |
+
default_weight_loader)
|
527 |
+
weight_loader(param, loaded_weight)
|
528 |
+
|
529 |
+
# If this function is called, it should always initialize KV cache scale
|
530 |
+
# factors (or else raise an exception). Thus, handled exceptions should
|
531 |
+
# make sure to leave KV cache scale factors in a known good (dummy) state
|
532 |
+
def load_kv_cache_scales(self, quantization_param_path: str) -> None:
|
533 |
+
tp_size = get_tensor_model_parallel_world_size()
|
534 |
+
tp_rank = get_tensor_model_parallel_rank()
|
535 |
+
for layer_idx, scaling_factor in kv_cache_scales_loader(
|
536 |
+
quantization_param_path, tp_rank, tp_size,
|
537 |
+
self.config.num_hidden_layers,
|
538 |
+
self.config.__class__.model_type):
|
539 |
+
if not isinstance(self.model.layers[layer_idx], nn.Identity):
|
540 |
+
layer_self_attn = self.model.layers[layer_idx].self_attn
|
541 |
+
|
542 |
+
if is_hip():
|
543 |
+
# The scaling factor convention we are assuming is
|
544 |
+
# quantized_value * scaling_factor ~= true_value
|
545 |
+
# which is consistent with the practice of setting
|
546 |
+
# scaling_factor = tensor_amax / FPtype_max
|
547 |
+
scaling_factor *= 2
|
548 |
+
if hasattr(layer_self_attn, "kv_scale"):
|
549 |
+
layer_self_attn.attn._kv_scale = scaling_factor
|
550 |
+
else:
|
551 |
+
raise RuntimeError("Self attention has no KV cache scaling "
|
552 |
+
"factor attribute!")
|